This piece was written in June 2023. An edited version was published in the Summer 2023 edition of the Society of Author’s magazine, The Author.

The question of how humans coexist with machines has long vexed all kinds of people, including — but not only — computer scientists, science-fiction writers and management consultants. Recently that preoccupation has become a little more widespread. Thanks to the astonishingly effective PR of a few technology companies, artificial intelligence has made newspaper front pages, filled breakfast TV tickers and created enough speculation on social media to keep data centres all over the world humming at full capacity. Generative AI is the apocalyptic monster of the moment and — if the big tech spin doctors are to be believed — it won’t just eat everyone’s lunch, it will also gobble up the rest of humanity.

However, this preferred corporate future — in which technology is recognised as the most superlative force of our age — is not inevitable. Automation and machine intelligence will almost certainly change the ways we live, work and communicate to some degree over the next decades, but it’s important to remember that technological change depends on people to make it happen.

We can and should shape and choose the way those changes play out, and to do that it’s important we understand them. Overall, it seems likely that the biggest issue AI creates for writers is not a crisis of creativity, but one of commercial terms. Automation is deeply unlikely to replace human imagination or sensemaking, even in the long term, but it is probable that the corporate machinations of the companies developing those technologies will continue to shift and undermine existing business models. Big tech companies become big for many reasons, and as Cory Doctorow and Rebecca Giblin’s recent book Chokepoint Capitalism outlines, one reason is their ability to capture the value of creative content.

Don’t believe the hype?

Current levels of fascination with AI are perhaps unusually high in media and political circles, but it is worth remembering that hype and new technologies have a history of going hand in hand.

Brent Goldfarb and David A. Kirsch examined 150 years of technological innovation for their 2019 book Bubbles and Crashes. When they started looking for trends they noticed that some of the most talked about technologies were also the ones that were the least well understood, with the least obvious market fit and the most varied range of use cases. Typically, they saw, new technologies that could be used in many different ways attracted more storytelling and speculation; these higher levels of speculation often led to higher levels of investment, which in turn resulted in the founding of more specialist companies. The recent AI spiral is the latest iteration of this hype cycle: other technologies that have recently exploded into public consciousness, before stabilising and receding, include Web3 (remember NFTs?) and the metaverse.

Perhaps one reason AI speculation is so captivating is that it has a rich history. While consumer technologies and social networks have come and gone, the potential for humans to create other kinds of intelligence has long been a compelling spur for both imaginative and scientific inquiry. Even the most rational and enlightened computer scientist cannot, it appears, always resist the urge to become a “Modern Prometheus”, and in the current financial and political environment the rewards for achieving this godlike status are considerable.

Another reason is that many of current assumptions about technological development draw on Victorian conceptions of technological mastery, rooted in a very technocratic view of progress. As Davids Wengrow and Graeber write in The Dawn of Everything:

over the course of the nineteenth century, almost everyone arguing about the overall direction of human civilization took it for granted that technological progress was the prime mover of history, and that if progress was the story of human liberation, this could only mean liberation from ‘unnecessary toil’.

The aspiration to remove effort assumes all work is “unnecessary toil” and does not allow space for sensemaking, critical understanding or the pursuit of joy beyond pure leisure. Applying the same logics to all work assumes that, as humans, we already know enough, have produced enough knowledge, and can continue to exist in the world simply by recapping and regurgitating the understanding we have amassed to date. But if we leave communication, creativity and knowledge production up to bots and algorithms and the people who make them, then we risk consigning humanity to existing in a mise en abyme, trapped within the confines of a reflection of a reflection of a reflection of the knowledge we once held.

The Imitation Game

Computing pioneer Alan Turing’s 1950 paper “Computing Machinery and Intelligence” explored the question “Can machines think?”. To determine if this were true, Turing sketched out an adversarial process called “the Imitation Game”, designed to test the cognitive capacities of a computer.

Now better known as the Turing Test, the game was created to see whether a human questioner could tell the difference between a human and a computer and it has since passed into popular imagination as a shortcut for invoking sentient machines. However, Turing was describing something quite different to the conscious humanoids that we know from Star Trek or from movies like Ex Machina: the Imitation Game was not designed as a benchmark for the levels of consciousness or humanity a computer can display, but as test to measure how good a computer is at pretending to be a person. And just as putting on a pantomime horse outfit doesn’t turn two people into a horse, when a computer pretends to be a person is not the same as becoming a person.

Computer scientist Meredith Broussard explains this disconnect between the AI we have and the AI we imagine in Artificial Unintelligence. Although some techno-optimists believe its creation is still possible, Artificial General Intelligence — in which machines achieve sentience and robot butlers become our friends — is still mostly the preserve of Hollywood. The technologies we have now are grounded instead in Narrow Artificial Intelligence, which is based on Turing’s aspiration for imitation.

Narrow AI is most easily understood as a “mathematical method for prediction”, which Broussard calls “statistics on steroids”. As such, when ChatGPT writes a sentence, it is echoing sentences that have been written before.

Human imagination and understanding is more plural and diverse than this, and is rooted in our conscious and physical interrogation of the world around us. Neuroscientist Anil Seth supposes that human consciousness is deeply rooted in our physicality; riffing on the Cartesian idea of the beast machine, he suggests that “We are conscious selves because we too are beast machines — self-sustaining flesh-bags that care about their own persistence.” This embodiment is vital to our ability to be and create in the world around us, and is one of the things that differentiates what happens when a machine parses information and a human reads.

When a person reads, we can turn the signs and signifiers on a page or a screen into imaginary worlds and concepts that uniquely draw together our context, our personal experiences, our beliefs and our knowledge. When a machine “reads”, it infers meaning from the words on the page and from the way those words have been used in other accessible data sets, but it does not (yet?) have a grasp of what those words mean in the world. As such, current forms of Narrow Artificial Intelligence do not make meaning, they mimic a human making meaning. This impressive but superficial linguistic facility — which leads to computer-generated content that seems convincing but is ultimately meaningless — led computational linguist Emily Bender to coin the term “stochastic parrot” to describe the limits of this mimicry.

Bender is co-author, along with computer scientists Timnit Gebru, Angelina McMillan-Major and Margaret Mitchell, a 2021 paper that explores the difference between understanding language (which humans do) and simply processing it (currently the domain of computers). Called “On the Dangers of Stochastic Parrots”, their analysis raises the fact that humans’ ability to find meaning means we might actively seek it out when reading computer-generated text. This is one reason that the outputs of Narrow AI can seem so compelling: why our eyes will hop over the extra thumbs generated by Midjourney or seek cogence in text written by bots. One of our gifts as humans is that we want things to make sense, and so we make sense of them, and infer sensemaking capabilities where none may exist.

Short-term risks

This is not to say that the current business models and methods for creating artificial intelligence do not create some immediate challenges for writers, knowledge creators and creative practitioners. In the short term, the business of writing is likely to be exposed to several stresses.

The most pressing of these is copyright. Artificial intelligence requires large data sets in order to learn, and those datasets are built on words and pictures that humans have created or had a hand in creating. This means that works generated via artificial intelligence are likely to be derivative, and — at the time of writing — the burden for proving this rests with copyright owners, who are expected to trawl the Internet looking for evidence of their work.

Amassing large data sets also makes it easier for artificial intelligence to create more formulaic texts, the kinds that might be relied upon to provide short-term, dependable sources of income. This reformulation of this market is likely to take place in a dispersed way, appearing to go slowly for a long time and then bedding in all at once. Probably many readers of this piece will have already heard of a creative practitioner in their network being displaced by an automated tool to do a small job, and the normalisation of this practice will ripple through different sectors at different rates, before becoming rapidly established itself as a norm. Longer term this will probably lead to the creation of new jobs and embed increased collaboration with automated tools into existing ones, but in the short term it is likely also make the day-to-day hustle of making a living a harder and more complex for some writers. Getting to an equitable settlement around copyright will create some useful friction, slowing down the speed of this change and, in an ideal world, also creating space for retraining and renegotiation of contracts.

There are currently a number of legal cases and consultations in progress that will set precedents and initiate frameworks for how human input to artificial intelligence is credited and paid for. Giblin and Doctorow’s Chokepoint Capitalism unpacks how big tech has already mined human creativity to build existing digital business models for streaming and social media, and it seems unlikely that the current battle will be the last. It will, however, be a decisive one, that will help determine the commercial value of human creativity.

Perhaps the most important lesson to be learnt from the digital revolution is how important it continues to be for knowledge producers and the creative industries to stand together and act collaboratively and collectively to establish a fair deal for creativity. Technology companies can wield extraordinary resources of money and power to assure the best outcomes for their business models, and are unlikely to be defeated by a single courageous campaign or campaigner. A united front is needed to ensure protections now and in the future; as the capabilities of artificial intelligence improve, new challenges will emerge, but ensuring copyright works will help put in place foundational protections for writers and help establish some boundaries for big tech companies and the technologies they bring to market.

No technology is inevitable. Artificial intelligence won’t replace humanity, but it is likely to change the nature of some jobs and many business models; these are not godlike entities, but human structures and processes. As such, the immediate challenge is not to be overcome by fear of AI or its mythical capabilities, but to deliver collective action that ensures human intelligence and creative labour is not subsumed by short-term commercial concerns

This article draws on research conducted for Coldicutt R, Williams A, and Barron D, (2023) ‘The Networked Shift: A Creative Industries Foresight Study’. London: Careful Industries, commissioned by MyWorld and the Creative Industries Policy and Evidence Centre.

I went to a Q&A by the “godfather of AI” Geoff Hinton the other night, and was invited to a (mostly) academic dinner afterwards. In the slightly awkward period at the beginning when we were milling around (also known as “pre-dinner drinks”) I started chatting to a very eminent computer scientist who had retired some years ago, having mostly – I gather – worked in academic labs.

By way of making conversation, the Very Eminent Computer Scientist asked me what I “do”. This is an oddly complicated question at the best of times, because I’m a chronic overthinker, don’t have a convenient Richard Scarry job title, and I do a few different things. I’m also an anomaly in that kind of setting as I’ve been working in technology for almost 30 years without ever doing the sorts of things that generally equate to status: I’m not an academic, I’ve never worked at Google, and I’m a very long way from being fantastically rich. However, when people make polite conversation before dinner, they don’t want a whole spiel about how, actually, you work in an emerging field in which job titles are not yet fully formed and your practice is angled towards realising an inclusive feminist vision of digital technologies because, well, life is short and people are hungry, so instead I said, “I help people understand how technologies work in the world” and dropped the vague “tech ethics” catch all, and he replied, “How interesting, and, may I ask, what qualifies you to do that?”

Being asked by a professor “what qualifies you to do that?” while standing in the Senior Common Room of a Cambridge college is quite daunting, so I emitted a vaguely incoherent freefall of word association – the kind you kick yourself about while replaying a slew of potential pithy aperçus. I explained that I’d spent twenty years making and commissioning digital products and services, some of them used by millions of people, and so my practice was based on observing what happens to a technology when it goes into the world: how it’s adapted and changed, and how every technology is really unfinished until it’s used by people. Mercifully, at that moment, we were ushered into dinner, but “what qualifies you to do that” stuck with me, and I wished I’d had a better answer.

The question of “what qualifies you” to understand a technology is particularly relevant at the moment, as we enter the nth week of Sam Altman’s AI Hype Roadshow, a cavalcade of open letters and AI doomspeak from World-Leading Authorities, in which the term “AI” has been a compelling vehicle for a wide range of as-yet imaginary concepts.

In this instance, the ability to understand a technology is neither here nor there, because the point has not been to discuss any of the relevant technologies. Instead, the project of Altman and his merry band of doomsayers appears to be to capture power and create obfuscation by making new myths and legends. If there has been a teachable moment, then the lesson has not been one about the potential of technologies but about the importance of media literacy.

And this is by no means a new move, it just happens – this time – to have been astonishingly effective. For several decades, tech companies have been aware that political influence is as important as technological innovation in shaping future market opportunities: from tactical advertising to political lobbying to creating well-paid public-policy jobs that have improved the bank balances of many former politicians and political advisers, the importance of getting in first with compelling political story has played a critical role in creating, expanding, and maintaining their incredibly lucrative markets.

The current “existential threat” framing is effective because it fits on a rolling news ticker, diverts attention from the harms being created right now by data-driven and automated technologies and it confers huge and unknowable potential power on those involved in creating those technologies. If these technologies are unworldly, godlike, and unknowable, then the people who created them must be more than gods; their quasi-divinity transporting them into state rooms and on to newspaper front pages without need to offer so much as a single piece of compelling evidence for their astonishing claims. This grandiosity makes the hubris of the first page of Steward Brand’s Whole Earth Catalogue seem rather tame, and it assumes that no one will pull back the curtain and expose it as a market-expansion strategy rathe than a moment of redemption. No one will ask what the words really mean, because they don’t want to look like they don’t really understand.

And yet, really, it’s a just a narrative trick: the hidden object is not a technology, but a bid for power. This is a plot twist familiar from Greek myths, cautionary tales and superhero stories, and it’s extremely compelling for journalists because most technology news is boring as hell. Altman’s current line is roughly, “please regulate me now because I’m not responsible for how powerful I’m going to turn out to be – and, oh, let’s just skip over all the current copyright abuses and potentially lethal misinformation because that’s obvs small fry compared to when I accidentally abolish humanity”. If it reminds me of anything, it’s the cartoon villain Dr Heinz Doofenshmirtz from Phineas and Ferb, who makes regular outlandish claims before trying, and failing, to take control of the Tri-State Area. The difference is, of course, that Phineas and Ferb always frustrate his plan.

My point is not that so much that we need Phineas and Ferb to come and sort this all out, but that we need to stop normalising credulity when people with power and money and fancy titles say extraordinary things. When I went to Hinton’s Q&A in Cambridge last week, he spoke with ease and expertise about neural nets, but admitted he knows little about politics or regulation or people beyond computer labs. These last points garnered several laughs from the audience, but they weren’t really funny; they spoke to a yawning gap in the way that technology is understood and spoken about and covered in the media. Computer science is a complex discipline, and those who excel at it are rightly lauded, but so is understanding and critiquing power and holding it to account. Understanding technologies requires also understanding power; it needs media literacy as well as technical literacy; incisive questioning as well as shock and awe.

If there is an existential threat posed by OpenAI and other technology companies, it is the threat of a few individuals shaping markets and societies for their own benefit. Elite corporate capture is the real existential risk, but it looks much less exciting in a headline.

This blog post is the first in a series I’m writing as a Citizens Advice Policy Associate, exploring the social impacts of online choice architecture for consumers.

Modern digital design tends to optimise for speed, simplicity and stickiness.

When we use an app or visit a Web site where we might spend money, every click and every cue on the page will have been refined in the hope of converting us into valued (and valuable) customers. Most checkout journeys and one-click shopping experiences are accompanied by a stack of metrics that are monitored to make sure that we, the users, fulfil as many business goals as possible. Our bounce rates are minimised, our cart abandonment rates reduced, and we are kept whooshing through the conversion funnel — distracted only by cross-sells and upsells — until we’ve completed our transaction(s) and consented to receive emails about related products and services. The convenience generated by this optimisation is often regarded as a feature, not a bug.

In December 2022, Citizens Advice published “Tricks of the trade: How online consumer journeys create harm and what to do about it”. This report shared the results of a large-scale experiment on the social impacts of frictionless consumer journeys. The team found three high-risk areas — Buy Now, Pay Later options at checkout; online gambling; and auto-renewing subscriptions — and uncovered some stark truths about purchase paths that encourage us to make decisions on the fly. In particular, the research revealed that:

• 1 in 4 people have made online purchasing decisions they regret
• UK consumers spend £0.5bn a year on subscriptions they don’t realise will auto-renew
• 41% of those surveyed think websites make it too easy to “make the wrong choice”

More generally, the research also showed that some online consumer journeys contain design patterns that actively encourage disinhibition, resulting in more spontaneous, less cautious behaviour, often by people in vulnerable circumstances.

Off the back of these findings, Citizens Advice are calling for an outcomes based framework that requires businesses to put consumers at the heart of design. Many digital business teams and specialists will claim that consumers are already at the heart of their design — but not every piece of user-centred design is created with our wellbeing in mind. This blog post explores some of the industry norms that make customer journeys extremely easy to use and intentionally hard to understand, and explores whether repurposing the goal setting technique Objectives and Key Results (or OKRs) might be an effective lever for improving online consumer journeys.

Don’t Make Me Think

Frictionlessness is by no means a new trend in digital design.

Back in the early 2000s when I worked at BBC Online, almost everyone in our office had a copy of Don’t Make Me Think by Steve Krug on their desk. Published in 2000, long before any of us had a smartphone, the book was an early touchstone for people creating Web sites. Full of good lessons about buttons, hyperlinks, navigation, and search boxes, the title of the book is also Krug’s “first law of usability” which, he explained:

means that, as far as humanly possible, when I look at a Web page it should be self-evident. Obvious. Self-explanatory.

In this context, “self-evident” and “self-explanatory” don’t mean transparent and understandable, but something faster and less distracting, more akin to pre-attentive processing, a subconscious process that allows us to do things like glance at the car dashboard or recognise road signs while driving. Krug’s aim in Don’t Make Me Think was to reduce the “question marks” that popped up over users’ heads as they navigated a site, because:

when we’re using the Web every question mark adds to our cognitive workload, distracting our attention from the task at hand. The distractions may be slight but they add up, and sometimes it doesn’t take much to throw us.

And as a rule, people don’t like to puzzle over how to do things. The fact that the people who built the site didn’t care enough to make things obvious — and easy — can erode our confidence in the site and its publishers. (p. 15)

Krug’s aim was for Web pages to “work most of their magic at a glance” (p. 19). This glanceableness is really useful in things like map interfaces, checking your bank balance, or when you want to see which song is playing on Spotify, but it’s not always what’s needed to support a complex decision, like making a purchase or signing up for a service. As designer Matt Jones said over a decade later, this kind of “magic is a power relationship”, and it needs to be used sparingly.

The first edition of Don’t Make Me Think was written when ecommerce was in its early days, Web site navigation could be labyrinthine, and digital literacy could not be assumed. And Krug leans into the value of simplicity. Chapter 4 — “Animal, Vegetable, or Mineral? Why users like mindless choices” — explains why it’s important to create clear, certain choices, even in ambiguous situations. He goes on to justify this by referencing the party game Twenty Questions, citing how everyone accepts that

anything that is not a plant or an animal — including things as diverse as pianos, limericks and encyclopedias, for instance — falls under ‘mineral’” (pp. 41–2)

The aim of this plea for simplicity was to stop Web teams showing the internal complexity of their organisations to their users. This was surprisingly common in the early days of the Web, and the conventions championed by usability pioneers such as Krug, Jakob Nielsen, and Jesse James Garrett have made surfing the Web much easier and more accessible for everyone. But this approach also meant that it quickly became commonplace for designers to impose choices and architectures that were easy to use but difficult to understand.

Two decades later, this kind of simplified framing doesn’t just mask complicated org charts and upselling models, it also often sits atop algorithmic recommendation systems. And while ease and convenience are still sought after, many financial transactions are much more complex than a game of Twenty Questions.

Do the Hard Work to Make it Simple

One reason this search for digital simplicity has persisted is because few digital experiences come with an instruction manual. As users, we are all expected to make the best use of the available prompts to find our own way.

This universalism is one reason digital products and services are often thought of as democratising and accessible; certainly, most smartphone users now expect that what designer Don Norman termed the “frustrations of everyday objects” will be smoothed away so that most interactions are as easy as possible to complete.

There are all kinds of benefits to this. Certainly, no one needs it to be harder to pay their taxes, read a map, find accurate health information or complete a regular supermarket shop, but that doesn’t mean there aren’t other situations where it might be useful to have more space to deliberate before making a final choice — or that the norms of frictionless design don’t sometimes become deceptive or manipulative.

Designer Harry Brignull has been collating examples of deceptive design* for well over a decade, and the Deceptive Design Hall of Shame documents some of the many tricks deployed by well-known businesses to lock us into subscriptions, extract unnecessary data, and persuade us to sign up for services we don’t need. These patterns often come across as sleight of hand — a button or a checkbox at the end of another journey, an opt-out rather than an opt-in — and are calculated to appear when we’re least expecting them.

Digital products and services cut across many traditional regulatory verticals, which mean these design patterns are not confined to a single market segment or type of interface. They have been spotted in such diverse settings as software and app subscriptions, financial products, social media platforms, and in all kinds of ecommerce settings; their consequences can also be different in different contexts and so take a long time to come to light. A recent US Federal Trade Commission paper, “Bringing Dark Patterns to Light”, offers this context:

Because dark patterns are covert or otherwise deceptive, many consumers don’t realize they are being manipulated or misled. Workshop participants theorized that even when consumers do realize they have been deceived, many don’t report their experiences, some out of an unnecessary feeling of embarrassment at being tricked

To understand why dark patterns exist, however, we need to look beyond design, and get to grips with the business model.

Business model-centred design?

Much of the language of digital design is calibrated to make it sound as if we, the users, are the ultimate beneficiaries. But ultimately the design patterns that nudge and encourage us along the purchase path aren’t there to meet our wider “user needs” such as health and wellbeing; they exist to anticipate our immediate needs as people who might be about to spend money. In some contexts, what is often called user- or customer-centred design has actually been created to meet the needs of the business model. It’s business-model centred design, with a side of consumer ease.

For instance, Amazon’s famous “Customer Obsession” is not a humanitarian mission, but a business strategy to ensure the company delivers seamless customer experiences around the things they identify as being “most valuable” — or, in other words, most easy to monetise.

If being “user-centred” or “customer-centric” actually means “encouraging us to the end of the purchase path as quickly as possible, and upselling some additional items along the way”, then it’s arguable whose needs it really prioritises.

In 2021 journalist Karen Hao’s investigation into Facebook uncovered a devastating example of user behaviour being optimised to service a business goal with harmful consequences. Although this is not a user journey that necessarily ends in a purchase, it’s illustrative of how a focus on meeting metrics can have a distorting effect on design decisions:

Their goal, among other things, was to increase a metric called L6/7, the fraction of people who logged in to Facebook six of the previous seven days. L6/7 is just one of myriad ways in which Facebook has measured “engagement”… Now every user interaction once analyzed by engineers was being analyzed by algorithms. Those algorithms were creating much faster, more personalized feedback loops for tweaking and tailoring each user’s news feed to keep nudging up engagement numbers…

But this approach soon caused issues. The models that maximize engagement also favor controversy, misinformation, and extremism: put simply, people just like outrageous stuff. Sometimes this inflames existing political tensions. The most devastating example to date is the case of Myanmar, where viral fake news and hate speech about the Rohingya Muslim minority escalated the country’s religious conflict into a full-blown genocide.

Hao’s aside that “people just like outrageous stuff’ cuts to the quick of the potential regulatory dilemma: a design pattern that seems to make things more fun or convenient for one person may well create unacceptable risk for another, meaning it can take a little while for a harm to emerge at scale, and even longer for sufficient evidence to be gathered to prove its impact. Meanwhile digital design practice is constantly changing, and any effective regulatory intervention would need to be systemic, anticipatory, and extremely flexible — or else risk going out of date before even being implemented.

So what would be an effective regulatory lever?

The business model is queen

Although digital interfaces might dominate our online experiences, it’s important to remember interface design is only one part of a digital consumer journey. The business model is by far the most influential component, and the one with the most direct influence over what we see on the screen.

In 2023, it is not news that digital business models can have shocking effects. Digital businesses — particularly ones that don’t have to contend with physical logistics — often make a virtue of their ambitious approach to growth and scale, and this appetite for expansion can lead directly to extractive, and sometimes deceptive, interface design and marketing campaigns.

Objectives and Key Results (OKRs) are one technique used to turn business models into shared, executable sets of goals. When used well, OKRs are an incredibly effective way of getting teams to deliver against stretch targets, and they have become synonymous with what John Doerr, author of Measure What Matters: OKRs — The Simple Idea That Drives 10x Growth, calls “exponentially aggressive goals”.

This boundaryless approach to growth and delivery has been one of the enablers of Big Tech’s hyperscale. Doerr’s book contains many examples of what he calls the “radical, high-risk action” tech teams have taken to achieve “Big Hairy Ambitious Goals”. This kind of expansive, pro-growth thinking means that high-risk, high-reward choices can quickly become normalised in digital product development, sometimes at the expense of abiding by social norms.

Over the years, Netflix has, unusually, set and shared these kinds of hyperbolic goals with the public. In 2017, CEO Reed Hastings claimed sleep was one of the streaming platform’s major competitors; the screengrab above is from the company’s 2019 investment statement, setting out their aim to compete with all of a viewer’s leisure activities, including “going to dinner with friends or enjoying a glass of wine with their partner”. At the time of writing, in January 2023, this has a slightly milder but still “hairily ambitious“ formulation:

We compete for a share of members’ time and spending for relaxation and stimulation, against linear networks, pay-per-view content, DVD watching, other internet networks, video gaming, web browsing, magazine reading, video piracy, and much more. Over the coming years, most of these forms of entertainment will improve.

Not all digital businesses operate at this level of inflation, but this kind of goal setting has the effect of shifting the Overton Window across the industry, normalising over-inflated goals that are unmoored from social consequences, so that everyone is fighting to gain more repeat visits, more engagement, more time in app or on site, and more completed purchases.

In my previous job, at responsible tech think tank Doteveryone, we addressed this by creating a tool called Consequence Scanning — a way for product teams to build a reality check into their delivery schedule and understand how their “key results’’ might have much broader implications for other people and the planet. Digital metrics tend to be very insular: they don’t show the people making the product or feature much about real-world impact or social consequences, they just show what people are doing on the site or in the app, and indicate whether or not they are achieving their engagement targets and realising their business goals. There is no real excuse for this kind of introspective approach, but leaving it behind will require a shift in culture across the industry. Introducing new regulatory scrutiny would be one way to expedite this.

An Outcomes-Based Framework

Regulation that encourages good outcomes, regardless of the technological or the social context, will be much more sustainable than an approach that prioritises banning specific practices. This may seem like a complicated approach that relies too much on anticipation, but it’s not that different to setting business goals, such as the objectives and key results described above. Making regulatory standards a natural part of the product development process, not just a compliance problem, will also make them easier to adopt and adapt to and it’s likely to lead to genuine service improvements rather than incremental adaptations. An outcomes-based approach is also more durable in the face of technical change than banning specific interface design components and would also make it easier to identify what has gone wrong when those thresholds are not met, and allow the same standards to be implemented across a wider range of technologies.

The recent progress of the UK Online Safety Bill has shown some of the limitations of naming and banning specific harms. Digital harms often take different forms in different contexts and can also take a while to fully emerge and become widely recognised and understood; meanwhile, changing technical capabilities can mean that the same kind of harm can quite quickly evolve and manifest in different ways. For instance, the Online Safety Bill refers to both “upskirting” and “downblousing”, but these are not the only forms of non-consensual and invasive image gathering, and the specificity of the current wording may turn out to preclude other similar types of photography that emerge over time.

Setting expectations around outcomes also has the potential to define what good looks like, not simply to name the bad. Banning specific design patterns makes it possible for businesses to implement similar, alternative patterns that achieve the same outcomes, whereas defining better outcomes might also, over time, deliver an overall uplift in the quality of experience and raise consumers’ expectations. The World Wide Web Accessibility Standard already works in this way: the rating system — crated by experts — sets an aspirational ceiling and a clear quality threshold. For businesses, the duty to comply with this is enshrined in the Equality Act. A similar model could be adapted to support online consumer journeys.

Vulnerable customers should not have to risk financial exploitation online at any time — let alone during a cost of living crisis. The sharp practices that lock us into unwanted subscriptions, unnecessary purchases, and make online gambling too easy should be named and shamed, and alternatives found, so that more of our own money stays in our own pockets.

Digital consumer journeys do not need to be extractive. Making them so is a deliberate choice, and it’s one we need businesses to stop making.

*This blog post from designer and ICO Fellow Caroline Sinders discusses the importance of replacing the term “dark patterns”, which has both racist and colonial connotations, with a more inclusive term, and debates whether or not “deceptive design” is a suitable replacement

Earlier this week, the 2012 Horse ebooks tweet “Everything happens so much” popped up again in my timeline, like a 10-year anniversary special. And it’s true: everything’s been happening so much lately, I wanted to pull together some things I’ve been thinking about and poke a bit at the overlaps and commonalities between them.

If there is a theme that runs through the following, it’s that 2023 will be full of quite mundane-seeming change. While we’re waiting for the next moonshot to save the planet or the next billionaire to go bust, we won’t much notice that our chicken wings are being delivered by a robot, but ultimately this kind of tech normalisation will have a bigger impact on the day-to-day reality of most people’s lives than any of the things peaking out on the hype cycle. I’ve grouped the observations under four headings — People working with machines; Polarisation and decentralisation; Inequality; and Regulatory entanglements — but there is plenty of overlap between those topics.

Also, a caveat: I’ve been ill for most of the last month (which, let’s be honest, feels very on trend) and am left with a bunch of lingering Covid symptoms that make concentrating hard, so I started this as a writing exercise and am now (in another very on trend, post-Twitter move) sharing it as a blog post. I’ve not run any of these thoughts through a fancy superforecasting process, it is just a note of how the world appears to me right now.

1. People working with machines

The generative tchotchkes will go mainstream

2022 has shown us that a lot of people really like playing with generative tchotchkes like Dall-E, Lensa, and ChatGPT. There’s something thrilling about the low-stakes death drive experimentation made possible by these limited attempts at human replacement, and they’re also a great vehicle for making clever jokes on social media.

Meanwhile, any technology that can render evermore infantilised, sexualised and suppliant depictions of women is always going to generate huge amounts of media coverage in which everyone looks extremely shocked about how on earth those boobs got so big on the made-up programmable lady servant.

Shipping these kinds of tools is a win-win for any company struggling to find market fit as an AI/metaverse start-up: they generate huge amounts of PR and name recognition while also delivering proof of buzz to investors. As such, it seems probable we’ll see a lot more of these tchotchkes in 2023, and they’ll rapidly settle into the free and very cheap cloud-based kit of parts that companies use to deliver their digital customer services.

Robust quality assurance models for these kinds of tools are still a long way off, and large data and language sets tend to normalise and encode the world view of a very small subset of society,* so it seems sadly probable that 2023 will see more than its fair share of Bots Gone Rogue. If I were a betting person, I’d put money on seeing some very stark racist, ableist and sexist outcomes from the overdeployment of these technologies very early in Q1 next year.

*There is plenty of accessible scholarship on the topic of bias in large data sets; Vinay Uday Prabhu and Abeba Birhane’s “Large image datasets: A pyrrhic win for computer vision?” is an excellent recent starting point.

Human customer service will start to denote artisanal quality or high-end luxury

Since the start of the pandemic, fast casual dining chains have been hiding in plain sight as pioneers of (often low-tech) automation. It’s probably only a matter of time before your Nando’s order arrives at the table on the back of a delivery robot, and this year UK chains including Leon and Itsu have been trying (not that successfully) to edge towards staffless, Amazon Fresh-on-a-budget self-service customer flows.

Just as in small-format supermarkets, where staff duties include servicing the needs of the grocery delivery apps and resolving the failures of the self-service checkouts, service staff in chain restaurants are likely to become machine monitors: there to manage fail overs and glitches rather than to cultivate relationships with customers.

Two outcomes of this will include the continued rise of the phenomenally boring job, in which humans are required to provide continuous partial attention to apps and robots, and the emergence of a very weird, slightly edgy ambience in low-staffed, unsupervised chain restaurants. Meanwhile, human customer service will come at a premium — a sign of either a small business struggling to get Zettle to work for cashless payments, or of a high-end luxury experience.

2. Decentralisation and polarisation

VCs will keep pumping money into Web3, missing the whole point of decentralisation

The Careful Industries team conducted a piece of research for the Frontier Tech Hub this year on how Web3 might contribute to solving the great global challenges. The closest we got to an optimistic answer was that — like many other new technologies — Web3 would deliver an “innovation uplift”, bringing new money and energy into the service of complex and existential problems.

It’s possible we disappointed our commissioners in failing to find a silver bullet application for these technologies, but we did spot something else: the fact that people are tiring of relentless centralisation, and are looking for new ways to gather and organise. In some cases we noted this as a response to the real and perceived failure of existing technical, political, and social infrastructures; in others, it seems motivated by a desire to find new ways of distributing power and assets in the face of climate injustice, deepening poverty, and financial exclusion. These social shifts seemed more significant to us than any of the technical attributes of Web3, and we explored the potential long-range social and political implications of this in our report.

Whatever the case, this urge to regroup is fundamentally at odds with the tendencies of investors to massively overinflate tech markets in search of scale. Overvalued tech bubbles are highly likely to continue to pop next year, but the most significant social shifts to decentralisation will happen well beyond the bounds of start-up culture.

We’re going to get more niche

Three decades of the World Wide Web have made it much easier for large numbers of people to find affinity with people who share their hobbies and interests.

As life gets harder and more expensive, finding deep affinity will become much more appealing for some, and we’re likely to see many more niche interest groups come to public attention in 2023. As trend forecaster Sean Monahan put it in The Guardian this week:

In retrospect, I believe the vibe shift is a return to fragmentation. Culturally, the 2010s were an era of centralisation. Subcultures died. Instagram reigned supreme.

The proliferation of niches and subcultures is different to polarisation, and not just restricted to fashion. The Culture Wars-ification of political discourse means that widespread polarisation has become accepted as a fact of post-2016 life, but the term is often misused to refer to any issue on which there is a lack of clear public consensus. However, failure to reach agreement is not necessarily an indicator of polarisation; it might also be a sign of diversification, reflecting a plurality of opinions or preferences that cannot be reduced to a headline or an answer in an opinion poll.

Defaulting to polarisation also assumes that all human life is arranged on a linear, left-right scale, but traditional party politics is only one (fairly limited) way to understand the world. After all, much of human life takes place outside of the artificial bounds of political affiliation.

There are, of course, a few hot button issues that provoke fierce binary opinions, but these tend to be outliers rather than indicative of the general state of popular opinion. As Bobby Duffy noted at the end of last year:

people of all generations are identifying more with their own group and differentiating themselves from the ‘other’ group

This process of identification is not always antagonistic, and overall it seems probable that we are actually shifting towards a more decentralised society, in which there are many different groups, all of which have greater affinity with their near neighbours and perceive significant differences with others.

Many factors will have led to this — including three years of pandemic life, an increased reliance on algorithmic content curation, and broader generational shifts in media consumption — but it is likely not an entirely new phenomenon, but the visible endpoint of a slow shift over decades.

One potential, optimistic outcome is that increased decentralisation could eventually drive a more inclusive society in which more people are free to make choices, more perspectives are deemed to be valid, and fewer people are expected to conform to the norms of the dominant group. Adapting to this will be difficult for traditional organisations and perhaps violent at some points, but the end result may well be that more people have the means to flourish and be self-determined. There are many challenges in the short term, and it’s likely 2023 will see significant cultural friction in response to increased visibility for more niche and subcultural groups.

3. Inequality

The billionaires will be forgiven, and get a second and third chance to spoil it all

Maybe not Sam Bankman-Fried*, but unless Elon Musk loses all of his money or goes to Mars, someone will keep handing him free passes.

*I mean, who am I kidding? Probably also Sam Bankman-Fried

The Hostile Environment will continue to be a testbed for inhuman uses of innovation

There’s not much more to say about this, and I don’t want to give anyone ideas, so I’m going to leave it at that. However, in case you can’t imagine what could be worse, this Mastodon post from Damian Williams and this from Evan Greer are two examples from outside the UK of ways “innovative” infrastructure can be easily misused.

4. Regulatory entanglements

Lastly, as 2022 comes to an end, there is unfinished digital regulation legislation on the books in the US, Europe, and the UK. How these different regimes interoperate will start to become clearer next year, and some of that will get pretty messy. This will have all kinds of consequences, but one specific thing is that it is likely to be tough on the digital rights sector: I hope I’m wrong, but during this period of adjustment it seems possible it will be even harder to get work funded that seeds alternative hopeful futures, as more effort is funnelled into supporting or responding to the legislative shifts. Of course, both things are important, but it’s just as important to start new things as it is to stop existing ones.

Earlier this year, I was part of a team working on a piece of research to assess the impact of safety tech on the UK digital economy. My role was to think about policy recommendations and framing but — as so often happens with this kind of project — the policy recommendations didn’t make it to the final cut.

The following are personal observations. This isn’t a comprehensive set of policy proposals, but some practical, achievable steps that are important to consider if safety tech is going to be deployed safely, in ways that benefit the people most likely to be affected by online harms.

This is a complex area and a short blog post, so I’ll lead with the recommendations, which are, I hope, relatively self explanatory, and then explain why these considerations are important.

1. Researchers, advocates and civil society innovators need to be well-funded and have formal channels of communication, influence and feedback with the safety tech sector
2. Someone watches the watchers — a safety tech standards and scrutiny body is created, prioritising lived experience of harm over industry representation
3. Public engagement and feedback is an ongoing part of the process, not restricted to consultation phases

What is safety tech?

A previous DCMS report describes safety tech as “technologies or solutions to facilitate safer online experiences, and protect users from harmful content, contact or conduct”. This is another way of saying that the term “safety tech” covers a lot of different things, of varying technical complexity and with a broad range of social, cultural, and political implications.

As in much online policy, everything that happens on a screen is bundled together here, but this clustering is not very helpful for thinking about governance.

In reality, national security concerns and individual anti-social behaviour have very different causes and remedies, but both are covered, as is the facilitation of online experiences (which might be anything from a text-based chat to an immersive game) and people’s’ “protection”, in itself a complex and contentious social concept. It also includes the work of the Internet Watch Foundation in detecting CSAM and what happens when someone reports a nipple on Instagram.

Age assurance — often considered a curative for many online ills — is also included. Digital age assurance is a complex problem in a country without a central identity system. Many of the proposed solutions (including, for instance, this report from the Children’s Commissioner) assume it is possible for a patchwork of platforms and third-parties to hold sensitive information about people’s online behaviour, or that it is possible for “estimation” technologies to assess whether a person is older or younger than 18. Age estimation is a growing industry that many companies will rely on as more stringent measures are introduced, but it sits atop a tangle of human rights and security concerns that are ambiguated by smooth processes, and widespread roll-out without sufficient diligence risks proliferating a new set of online harms.

Likewise, delegating content moderation to machines is complicated.

Automated content moderation is necessary, but is not always sufficient or problem free. (See Sarah T. Robert’s Behind the Screen for an introduction to the human costs of human moderation.)

At its most simplistic, the clash of the technical and cultural can be seen in what is now known as the “Scunthorpe Problem”, in which — in the mid-1990s — AOL’s application of bad language filters led to all mentions of the town of Scunthorpe being marked as offensive. While web filtering has improved a great deal in the last 25 years, it remains far from perfect.

The need for transparency, standards and oversight

Safiya Noble’s Algorithms of Oppression offers myriad examples of how “racism and sexism are part of the architecture and language of technology”. Algorithmic bias and the structural problems of using historical data to make decisions about the present or the future increase the likelihood that safety tech will replicate and deepen structural inequalities, including, but not limited to sexism, racism, ableism. This means, without sufficient guard rails, safety tech could end up harming the people it has been created to protect.

While the concept of social and technological bias is well established in discussions of AI ethics (see Meredith Broussard’s Artificial Unintelligence for a very readable primer on this), it is rarely covered in policy discussions of online safety. I touched on some of the challenges of executing algorithmic moderation in just and effective ways in written evidence to the Lords Communications and Digital Committee last year, but it remains a relatively niche concern in UK policy debates — despite its potentially chilling effect.

Abeba Birhane’s “Algorithmic Injustice: A Relational Ethics Approach” outlines one of the foundational problems of “fairness” in machine learning, which is that:

“the reality of the Western straight white male … masquerades as the invisible background that is taken as the ‘normal,’ ‘standard,’ or ‘universal’ position”

This is the same set of biases and cultural norms that led to crash test dummies being modelled on standard male physiques, meaning that safety measures are only designed for one section of the population. While the challenges this creates might present differently in different safety tech contexts, the fact that the Western straight white male is rarely the target of racist or sexist online abuse presents a problem for the rapid roll-out of safety tech. Mitigating this is essential.

Safety tech is a useful part of a holistic response to developing online trust and safety, but it is not and cannot be the only component. There is, after all, an irony to assuming that the problems caused by the unregulated deployment of technologies by private companies can be solved by the unregulated deployment of technologies by private companies. At the very minimum, there must be mechanisms for transparency, accountability, and redress, otherwise safety tech risks entrenching the very problems it has been invented to solve.

“Instead of seeing the corporate sector, military strength, government and law as the most important segments of society, deserving the highest level of wealth and power, a caring society might see that the tasks of bringing up children, educating its members, meeting the needs of achieving peace and treasuring the environment, and doing these in the best ways possible, should be that to which the greatest social effort of all should be devoted.”

Virginia Held, Ethics of Care (2006)

A truly caring digital society nurtures and grows spaces beyond the market, and imagines possibilities outside of the app store. Innovation in a caring society must not only generate profit but create better outcomes for everyone. This is possible, but it requires care for people and planet to be valued as highly as capital — and for wellbeing to be incentivised as much as shareholder return.

Consumer digital technologies tend to individualise and personalise, which in turn makes us all easier to monetise. The consequences of this individualisation are far-reaching, but they are as much a consequence of capitalism as they are of technology: after all, almost any goal or any outcome can be designed for. Meta’s goal to “maximize engagement” might be financially profitable, but it comes at the cost of increased “misinformation, extremism, and political polarization”. This societal debt is by no means paid off by their quarterly earnings; instead it is quietly, and repeatedly, written off.

It might seem that the close coupling of digital innovation with the market was inevitable, but our present reality is only one possible digital world — creating others is still possible, and there is little time to waste. Many digital worlds can and do co-exist, and it is not necessary to dismantle the platforms before building alternatives.

This essay begins with an exploration of how digital capitalism has minimised care and and ends with suggested next steps for building the foundations for what must come next, spaces for “alternative publics” that exist beyond the walled gardens of platform infrastructure. Cultivating the conditions for what can and should come next requires a systemic approach to nurturing digital infrastructure: for looking beyond capitalism and the constraints of the market, and prioritising care and public good.

Digital care as individualism

What do you think of when you think of digital care? Perhaps Paro the therapeutic, seal-shaped robot or the parental controls you put on your children’s devices. Perhaps it makes you think of the affirmations that pop up on Instagram, the check-ins you share with your close friends and loved ones or the well-earned Friday night takeaway that gets magicked to your door through an app. Or it might make you think of the alerts from your bank that tell you what you’ve spent, and how much money you do (or don’t) have left.

As service-design expert Lou Downe says, “Good services are verbs”, and digital care tends to be created in ways that make it easier for us to take active steps to look after ourselves.

In the UK, the state and a number of large institutions deliver certain kinds of digital care at a macro level, while the digital consumer market takes care of the micro, through apps and ecommerce and smart devices. There is not really a plan for this, a whole ecosystem map of what these combined services could or should look like, so as consumers we wander through, gathering what works for us as and when we can. In the language of digital transformation this is known as “disintermediation”, or unbundling, and its effects can be seen in many parts of life, from the disappearance of travel agents to the plethora of new entertainment providers.

In a healthcare context, digital care might be familiar to affluent, time-rich consumers as a journey that includes tracking steps, diagnosing symptoms on the NHS website, ordering bespoke monitoring tests from a healthcare start-up, clicking on an Instagram ad for dietary supplements, and eventually booking a video consultation with either your local GP or a pay-as-you-go private provider. But if you can’t afford a stable and secure home Internet connection, not only are these options unavailable, you will also have reduced access to the wider social determinants of health, such as cheaper goods and services, higher paying work, support from friends and family, and reliable information. There is no overall responsibility for weaving these digital care services together as a safety net; instead, they all exist to extend our personal responsibility — to make us do more with the little time we have.

Our smartphones help us to be simultaneously more self-determined and more extractive — and this is by no means a new digital phenomenon but the logical progression of market determinism. Writing in 1989, political scientist Joan Tronto observed that a disposition towards caring for others was in tension with market forces:

“to be attentive to the needs of others, one must relinquish absolute primacy of the needs of the self… The paradigm of the market requires putting one’s own interests first.”

Over the last three decades, digital product design has turned infinite attention to “one’s own interest” into a feature, not a bug. From personalised ads to pre-filled shopping baskets and optimised purchase paths, we are all now royalty in our own homes, served — for a small price — by Netflix, Uber, and Deliveroo. When we order something on Amazon, the ability to solve day-to-day problems by conjuring essential items to our door can feel very empowering, but we have no sight of the working conditions of the warehouse staff or delivery drivers who make that happen — and often-times our reliance on these services is increased by the pressure to optimise our time management and engage with the never-ending personal admin that our complex digital lives demand. Sociologist Arlie Hochschild observed in 1983 that, “in the modern market society… providers of services are expected to feign caring”, and we are complicit in maintaining that charade, as we assign star ratings to gig economy workers, rating how thoroughly and efficiently they anticipated our needs.

Our relationship with what media historian Rachel Plotnick calls the Power Button is not so new either. The “complete your order” button in an app is not materially different to the service button or bell in grand nineteenth-century homes that summoned servants to do their employers’ bidding. It’s an interface that obscures unseemly effort and creates an illusion of control, while simultaneously enforcing hierarchical power relations. The labour that takes place behind the button is out of sight and out of mind.

The patterning of this individualism goes further back still. Ruth Schwarz Cowan’s survey of domestic technology, More Work for Mother, examines how increased technological capability aided the privatisation of home life and extended the burden of the housewife, from pre-industrial societies on. As water pumps were replaced with private water supplies and communal ovens with family stoves, the edges of the household were drawn more tightly, and woman’s work became a fixture of that privatised setting, unstoried and unseen. Writing in 1983, Cowan described the modern housewife as:

“the last Jane-of-all-trades in a world from which all the Jacks-of-all-trades have moreorless disappeared; she is expected to perform work that ranges from the most menial of physical labour to the most abstract of mental manipulations and to do it all without any specialised training.”

Despite this range and complexity, care work remains low status and badly paid because it is still largely done by women, out of public sight. When turned into consumer goods, it takes the shape of convenience — cleaning products with miracle properties, pre-prepared foods, and robot vacuums that, coincidentally, share maps of your home with Amazon; as Cowan says, “in the West, over the last two hundred years, women’s work has been differentiated from men’s by being incompletely industrialised” — and the hardest and most human parts of the tasks are left undone.

Care is something that the market expects us to take for ourselves, while providing technologies that seal us ever further in our homes. From neighbourhood notice boards calibrated to create dispute to Ring doorbells installed to protect and surveille, digital technologies can be deployed to increase our privatisation, separation, and fear.

This is, perhaps, one endpoint of libertarianism: the freedom to be at home, alone, waiting for your Getir driver to arrive — reliant on consumer technologies, but independent from all else.

And although this is a much smaller world than the one envisaged by acclaimed technology visionary Stewart Brand in The Whole Earth Catalogue, it shares some qualities with that bold individualism. Brand sought connection to a cybernetic web of knowledge, hoping it would lead to total independence — what he termed a “realm of intimate, personal power”, in which “[w]e are as gods and might as well get good at it”. Fifty years on, this heroic vision is responsible for shrinking the potential of our digital society — optimising self-service, alongside small acts of personal digital care.

Digital Care as Community

“Care is everything that we do to maintain, continue and repair ‘our world’ so that we can live in it as well as possible. That work includes our bodies, our selves, and our environment, all of which we seek to interweave in a complex, life-sustaining web.”

Joan Tronto, Moral Boundaries (1993)

Care is not only a verb; it is also a value system and, according to Joan Tronto, “a survival mechanism for women and others who are dealing with oppressive conditions.” It is not just the work we do for ourselves, but the work we do for others, the bonds we build, and the trust we share. Care is a process, an escape, and a set of new possibilities. It is not a yellow thumbs up on Facebook or a remix on Insta, and it is not always visible.

So what does that mean? Where does that leave a reimagined, more care-full, digital world?

If capitalism individualises us, then care can bring us together — giving us cause to celebrate connection over engagement and relationships rather than subscriber numbers.

This might sound grand and impossible, but the Web was, quite literally, made for this; the metricisation, surveillance, and billionaires were added later. The Web as a place of connection is central to the concept of “hypertext”, and while it was conceived within the hierarchical confines of research academia, it was originally imagined as a relational space. This early sketch of the architecture shows a Web woven from the links between people and information, in which the person is just one node of many. By 2008, Berners-Lee had evolved that ambition even further, describing the Web as:

“really a system of people. Yes, it is enabled by technology … [but] we stopped thinking about the internet as the collection of computers and we stopped thinking about the Web as the collection of connected pages and now we think of the Web as humanity connected.”

Of course, with hindsight, by 2008 the lack of social and regulatory curbs and civic investment had already made this version of the Web impossible, but that does not mean some other versions of it cannot be realised in different forms.

The difficult thing, in 2022, is to imagine a full transformation of the status quo; a world in which all platforms are positive, and digital extraction is laid to rest. Regulation and legislation can and should moderate the platform’s worst excesses, but it is essential to also build what should come next.

One remedy for the extremes of our existing, individualised digital world is to create more balance, and nurture more alternatives. Rethinking access, hosting and hardware, and skills, as well as developing more sustainable and equitable approaches to governance and investment would help lay the foundations for this. The following set of pointers simply scratches the surface for what might be made possible with the creation of community-powered, responsible infrastructure.

1. Universal basic digital access

The first step is to rethink access: to decouple Internet connectivity from the market and make it a genuine public good.

Current connectivity models assume that the private household is the default billable unit, and impose artificial limits on an abundant resource. Two decades of relying on the market to deliver connectivity have shown how rapidly inequality can be created: neighbouring households might be affected by digital poverty or benefit from super-fast broadband, regardless of their need or proximity to broadband provision. A model such as place-based universal basic digital connectivity would enable access to the digital world beyond the constraints of the market, and enable wider access to both digital participation and production.

2. Decarbonised hosting and hardware.

It is also vital to invest in green infrastructure.

Legislatively, it is essential to enshrine the “Right to Repair” so that hardware can be mended and maintained. More people also need the capacity to fix and manage their devices, and these kind of practical skills could be diffused through reforms to the national curriculum and the creation of free civic training courses, based in community repair shops. Local refurbishment specialists should also be incentivised and supported through tax schemes, and given access to materials through an expanded network of e-waste collection and recycling points.

Decarbonised alternatives for compute power, data storage and cloud services should also be incubated with state support, and all public services transitioned to green alternatives as a priority. The Green Web Foundation’s call for a fossil-free Internet by 2030 should be supported by ambitious targets for UK businesses to transition to green digital infrastructure, with incentives and penalties put in place to encourage hardware reuse and migration to fossil-free services.

3. Decouple Digital Skills from STEM.

STEM skills are not always essential for all digital jobs. Two stark consequences of the sciencification of the digital world are the lack of regard for the social impacts of technologies, and the reinforcement of digital as an elite domain that reinforces sexist, racist, and ableist norms and standards. There is not space in this essay to detail this extensively, but examples and analysis are easy to find. Good starting points include Emily Chang’s Brotopia: Breaking Up the Boys’ Club of Silicon Valley, Christina Dunbar-Hester, Hacking Diversity: The Politics of Inclusion in Open Technology Cultures, Safya Umoja Noble’s Algorithms of Oppression: How Search Engines Reinforce Racism, Elon Musk’s Twitter feed (which I won’t link to), and almost every single article published in Business Insider.

This is not to say that innovation does not need computer scientists — of course it does — but a traditional science and technology background is not the only way to become a digital maker.

This is in part because digital products and services also need communicators, designers, researchers, anthropologists, coders, and creators of all kinds, and a focus only on STEM skills fails to grasp the importance of these multi- and interdisciplinary teams in creating good services. Moreover, the rise of No Code and increased availability of tools and services such as Glitch and Tensor Flow will, over time, lead to a relatively small proportion of the overall number of future digital roles requiring deep technical and computing knowledge. Instead, many more people will need technical confidence (or what Alix Dunn calls “technical intuition”) but not everyone will need to know how to make things from scratch.

The sciencification of workplace digital skills also fails to understand the different motivations that different people have to create. It assumes that a traditional academic and professional approach to acquiring skills and knowledge is the default way of entering what is still a nascent and deeply improvisatory field of practice. However, anyone engaged with digital culture will know that makers are all around them, and that a culture with some parallels to digital jugaad is alive and well in the Global North, outside the constraints of big corporate culture. Influencers might be the most visible face of this, but they are just the visible tip of the iceberg. As long as the Web has existed, fandom and a desire to communicate with others has been a driver for learning how to mod, hack, and code. For instance, Thomas Dolby tells the tale of how teenagers repurposed an engineer-to-engineer communications protocol on early Nokia phones, and accidentally discovered text messaging.

This often takes place outside of formal education (see, for instance, Sacha Judd’s paean to how loving One Direction was instrumental in teaching a generation of girls to code), but skills acquired this way do not have the same status as those supported by certificates. The rise of the creator economy demonstrates how ingenuity, craft, and creativity can be unlocked in ways not be imagined by those designing formal curricula. Broadening the pathways to applied digital skills by recognising that they are not synonymous with STEM would result in a more well-rounded, more diverse and representative workforce.

One way to achieve this is to modernise education policy to reflect the fact that digital is now a fact of life, not a specialised subject, and to weave digital production and creation skills across a lifelong learning curriculum. This would lead to a more socially and culturally representative digital workforce, drawing on a greater range of influence, which would in turn lead to fewer extractive products and services being created, and a greater diversity of more sustainable visions and ambitions being achieved.

4. Cultivate alternative tech, beyond the market.

The communities, makers, and creators who are building resources, strengthening democracies, sharing knowledge and information are mostly bootstrapped or battling for philanthropic support. If Patagonia can make the Earth its only shareholder, every VC can consider how to reshape their portfolio and deliver against wellbeing goals for people and planet. The beginnings of a plan for stimulating this are set out in The Case for Community Tech.

5. Normalise shared governance.

The most transformational part of Web3 culture is the will for transparency and collective decision-making. While the execution of this may still be imperfect, taking digital governance out of the board room and moving from a focus on compliance to one of collective good could be transformational for the Digital Commons. Nurturing this, and using it to develop shared governance methods that foreground care and equity, could be transformational for the waves of technologies yet to come.

These measures are initial steps that prioritise social and natural capital over money. They will not result in decacorns or increases in GDP, but they do provide some scaffolding for growing alternatives to some of the most ingrained problems of our current digital world, ones that foreground digital wellbeing, and decouple digital from the relentless pursuit of capital. As sociologist Nancy Fraser says in Rethinking the Public Sphere, “history records that members of subordinated social groups — women, workers, people of color, and gays and lesbians — have repeatedly found it advantageous to constitute alternative publics”; investing in alternative infrastructure that will support and grow these alternative publics will create spaces in which social justice is the norm, not the exception.

This is not just a strategy for digital care, but for a Feminist Internet — one that is plural, sustainable, and more representative of our whole human existence. As such, to finish, it seems appropriate to cite thinkers who are shaping the feminist digital past, and the possible, unfolding future.

Like Stewart Brand, feminist scholar Donna Haraway also took early inspiration from cybernetics, but the connected world she described in the Cyborg Manifesto feels more like an escape into extended consciousness than a method of control. Writing in 1985, Haraway saw the power of the Web not as being for everyone (as Tim Berners-Lee would later say), but as made of everyone:

“Technologies like video games and highly miniaturised televisions seem crucial to modern forms of ‘private life’ … I prefer a network ideological image, suggesting the profusion of spaces and identities and the permeability of boundaries in the personal body and in the body politic. ‘Networking’ is both a feminist practice and a multinational corporate strategy… If we learn how to read these new webs of power and social life, we might learn new couplings, new coalitions.”

In shaping the futures of technologies it is vital to acknowledge how our digital lives represent more than “the unitary self”. The potential of the Webs we weave remains infinite, unlocking the possibilities of connecting to, and forming, many other selves and possibilities. This, more than any capital return, is what makes technology truly irresistible to us, and makes it worth making it work.

Almost forty years later, Legacy Russell’s Glitch Feminism builds on this, and describes the necessity of a digital life beyond physical and gender boundaries:

“We want a new framework and for this framework, we want new skin. The digital world provides a potential place where this can play out. Through the digital, we make new worlds and dare to modify our own.”

A Feminist Internet is not a place of polarisation, but one of exploration and adaptation, in which new cultures, communities, and connections are fostered. It is possible to build it, we just need to start.

Originally published at https://www.careful.industries on October 26, 2022.

Sunshine Machines: Towards a feminist future of digital care was originally published in Careful Industries on Medium, where people are continuing the conversation by highlighting and responding to this story.

Community tech — technology that gives power and generates benefit for communities — is a vital part of the modern technology ecosystem.

While a small number of very large companies might seem to dominate the digital landscape, the reality is that the Internet is full of alternatives and possibilities — of people making and sharing things for collective benefit.

Today we’re launching The Case for Community Tech, which sets out a vision for how hardware and software created by, with and for community organisations:

• builds the resilience and impact of individual community organisations and the communities they are part of
• contributes to the growth of place-based communities
• promotes a more diverse and sustainable technology ecosystem

Pioneering organisations from across the UK are developing alternative, locally rooted solutions to everyday problems like food delivery, sustainable housing, and green energy. And the technologies these communities are using and creating help them deliver collective benefits, including local economic and social capital, while also promoting a more diverse and sustainable alternative technology ecosystem.

In the words of Carolyn Hassan, CEO of Knowle West Media Centre, this is technology that helps “value to stick to a place”.

Over the last year, the Promising Trouble team has met with and spoken to people from UK community organisations who are making and using technologies that generate benefit for and give power to communities. Working alongside Power to Change and Cassie Robinson, we have discovered what these organisations have in common, the challenges they face, and the larger societal benefits that their approaches unlock.

Innovation is not only something that happens at start-ups and in labs. It also happens in skate parks and community radio stations, in community energy companies and social care collectives, in community pubs and libraries. Community tech helps unlock the potential of everyone, not just entrepreneurs and academics, and helps strengthen the social fabric in trustworthy, beneficial ways. It can generate and retain economic and social value for communities, provide an alternative to big tech, and increase the resilience and autonomy of community organisations.

This report marks the launch of a programme of activity throughout autumn 2022, including the launch of a £400,000 fund, Community Tech: Makers and Maintainers on 6 October and a Connected People and Places report, featuring contributions from a number of international technical and social innovators.

Read the full report | Book tickets for the Connected People and Places virtual launch on 11 October | Sign-up for our Community Tech mailing list for more details of the fund and launch webinar

Originally published at https://www.promisingtrouble.net on September 14, 2022.

I read this post by Meg Conley this week and it stopped me in my tracks, particularly this sentence: “Care work is the anticipation of grief.” This post is a rumination on how that term applies to care in a very different context.

Being care-full

The research studio I work at is called Careful Industries. The name reflects the fact that the work we do is both deliberative and full of care: care as a craft, a political orientation, and an ongoing commitment to generosity.

Many of our projects involve looking ahead for our clients. We do research that anticipates the social impacts of technologies and develop relational programmes and strategies that, in the words of bell hooks, strive to “bring traditionally silenced perspectives ‘from the margin to the center’”.*

Anyway, as you can see, it’s hard to give that a pithy label and a lot of the commonly used terms don’t quite do the job: “digital ethics” are often subjective and sometimes an excuse for acting ahead of law; responsible tech doesn’t work because everyone should be responsible; and our particular kind of work doesn’t directly help people practice better digital self-care (although, if you want advice on that, get Seyi Akiwowo’s brilliant new book ).

But reading Meg Conley’s Substack, I realised that sentence — which relates to care in a totally different context — encapsulates something useful. Much of our work can be described as systemic digital care work in anticipation of grief.

Care as an anticipatory act

Conley’s framing seems revelatory because care work is often characterised as a bundle of physical acts and emotional feelings, but in reality it involves predicting eventualities, mitigating the mitigatable, and softening the blows of the unavoidable.

Care work is the anticipation of grief is a beautiful articulation of care as a thoughtful and a thinking practice; a constant loop of recognising and managing risk. Taking care involves noticing and remembering, writing lists and prioritising; doing the right things in the right order to get to the best outcomes in the circumstance.

When I remind my son to put a jumper in his school bag on a sunny day, it’s because I’ve looked at the weather forecast and know it will be colder later. Likewise, when we reply to a government consultation , it’s because we’ve looked at the proposals and know the outcomes will be unjust or unworkable. Both things occur in the hope of offering some protection against the future.

Not having to worry about taking care — not living in a constantly anticipatory space — is a privilege. It might mean that someone else cares for you, or that consequences cannot harm you. Perhaps you’re young and fearless, or older with established power. Maybe you consider yourself to be a rule maker not a rule taker, or your start-up is so high on VC money it feels as if the world is rising up to meet you and you’ll never need to look down again.

But — on a burning planet — exercising this kind of short-termist power is nostalgic and unsustainable. In the technology industry — which loves a quick win, a fast buck, and military metaphor — this is often mistaken for “innovation”, but it’s really just the patriarchy with added AI.

Innovation as care work

Innovation can also be sympathetic, additional, long-termist, and nurturing. It doesn’t need to involve “blitz-scaling” or “disrupting”: it can also involve replacing failing systems; removing barriers; creating ease; and retiring the old and midwifing the new .

This kind of careful innovation aligns more closely with how most of us live. Most people have to think ahead all of the time — perhaps because they care for other people, or because there is no one else to care for them. Taking care in everyday life is not an act of weakness, but a sign of preparedness; it is at the heart of all good services and is essential for renewable and sustainable policies.

Routine, anticipatory acts of care are often invisible. They might involve doing sums in your head in the supermarket, prioritising which bills to pay and/or which meals to skip. Maybe it’s getting a cab you can’t afford because you don’t want to walk home in the dark, or because you’re saving your spoons to get through the week. Whether it’s always carrying your ID in case you get stopped — or never carrying it for the same reason; keeping period products and a pack of painkillers in your bag at all times; getting the bus before the one that gets there on time because you’ll get the sack if you’re late; avoiding eye contact so strangers don’t speak to you in the street. One way or another, most people have a lot on their mind. Most people are taking care of a great deal of business just to get through the day.

Systemic digital care is the opposite of disruption — that doesn’t mean it’s easier. It involves helping traditionally powerful people find ways to give their power away or to share it with others; to make choices that will be rewarding and regenerative over time, but difficult and counter-intuitive in the short-term. It’s a process that involves both looking ahead and learning from history, developing new models and paradigms, and celebrating and centring inspirational and equitable ways of working.

There’s an extent to which we’re still working some of this out, and we don’t have all the answers, but care is also about learning and responding to feedback and continuing to try harder to get it right. Our aim at Careful Industries is to help more people make technologies that do not create grief — to champion products and services that put the concerns of people on the margins first, and to nurture new ways of innovating that are better for people and the planet.

REFERENCES

*This framing, via my colleague Dominique Barron, is from Shantel Gabrieal Buggs, Jennifer Patrice Sims & Rory Kramer’s paper, “Rejecting white distraction”, which in turn builds on bell hooks’ seminal Feminist Theory: From Margin to Center.

Originally published at https://www.careful.industries on July 2, 2022.

Systemic Digital Care was originally published in Careful Industries on Medium, where people are continuing the conversation by highlighting and responding to this story.

When Careful Industries started to develop the Civil Society Foresight Observatory for the National Lottery Community Fund, a part of the brief was to work out what a Foresight Commons could look like — some kind of shared space and resource that might inspire people from across civil society and beyond, and become a point of collaboration for funders and policy makers. As Cassie Robinson wrote in this post on Grantmaking for Horizon Three, the purpose of this was to contribute towards:

a future where everyone can thrive; where we reward redistribution and contributing to the commons rather than growth; where we regenerate and care for each other and for the earth, not extract; and where we look after each other with justice and solidarity, not charity.

This post is part of a series exploring why we think that is still important.

What is a civil society foresight commons?

A civil society foresight commons sounds a bit like a salad of different conceptual terms, so I’m going to start by explaining what it is.

Overall, it’s a space for creating possibilities: a place to show and share what might be probable and possible over different time horizons. And there is an urgency to this: as economist Adam Tooze says , “the polycrisis comes from all sides and doesn’t stop” — there has never been a better time to collect inspiration and intelligence for how to build what will come next.

And it is vital to turn some of the inchoate and informal expertise that exists in civil society into more tangible outputs that can, in turn, influence funders, policymakers and communities, to invest in the creation of alternative possibilities.

But let’s break that down a bit more:

Firstly, what is civil society?

To borrow the definition used by the Civil Society Futures inquiry:

Civil society involves all of us. When we act not for profit nor because the law requires us to, but out of love or anger or creativity, or principle, we are civil society… Whether we organise through informal friendship networks, Facebook groups, community events and protests; or formal committees, charities, faiths and trade unions, whether we block runways or co-ordinate coffee mornings, sweat round charity runs or make music for fun; when we organise ourselves outside the market and the State, we are all civil society.

And what is foresight?

The futurist Cynthia Selen describes foresight as a way of “coping with the future”. It doesn’t aim to predict what might come next; rather it is a practice that demonstrates what could come next — of articulating a few of the thousands of possible and probable eventualities.

Why does civil society need a different kind of foresight?

As we discuss in A Constellation of Possible Futures , a lot of “official” foresight activity is commissioned by either governments or businesses, generally to create some kind of strategic advantage. That kind of official foresight is also likely to be conditioned by what Prof. Andrew Stirling calls “incumbent interests” .

The purpose of this kind of official foresight might be to anticipate which technological threats and opportunities are coming around the corner, or to generate some other kind of strategic or competitive advantage. This kind of foresight has a number of forms, but two of the most influential are: internal documents with “confidential” stamped at the top, and shiny reports that make confident predictions about things that matter to shareholders, such as “the future of retail”.

But, of course, the world is made up of more than national defence strategies and 10-year plans for shareholders. There is an infinite richness beyond anticipating new payment methods and possible causes of unrest; as the polycrisis continues to unfold, civil society is often asked to bridge the gap and improve the status quo — but rather than propping up the present, civil society can and should help to forge alternatives.

Why does it need to be a commons? And what is a commons anyway?

Two reasons: the first is to resist capture, and the second is to enable collaboration.

One way to understand the kinds of “official” futures I’ve described above is that they enclose the future — they represent the capture of the future, a shared public good that was, in the words of Charlotte Hess and Elinor Ostrom, “previously uncapturable”.

Public agency to imagine and create the future is taken away if huge swathes of possibilities are privatised and driven by technological change and capital returns.

Our contention is that trying to contain the future in formal reports and “eyes only” PowerPoint presentations puts boundaries up around stories and concepts that could and should be shared with and co-created by broad inclusive communities. Creating a knowledge commons is a liberatory act that means modelling possible and plausible futures can both happen in public, and benefit from the kind of governance and scrutiny that will ensure the process is, “managed, monitored, and protected, to ensure sustainability and preservation.”

Civil society foresight should also be public and plural; collaborative and transparent; sustainable and accountable. And, as David Bollier says in The Growth of the Commons Paradigm ,

many information commons exemplify what some have called “the cornucopia of the commons”… The operative principle is “the more the merrier.” The value of a telephone network, a scientific literature or an open source software programme actually increases as more people come to participate.

If you’re a funder thinking about the future and commissioning long-range foresight, how might that foresight become more powerful when shown in relation to the other possible futures that will unfold around it? How might the ways we all imagine and shape what comes next, be transformed by looking ahead together, and working together, to understand what needs to be in place to help them come to pass?

What’s next?

Through developing the relational foresight methodology , the Careful Industries team have explored how to hold a space in which many futures are possible — that doesn’t try to come to resolution, that holds disagreements and many possibilities. Our next job is to find ways to expand that space so that it is welcoming and shareable — a space for many futures to come together in a relational dialogue.

Originally published at https://www.careful.industries on June 24, 2022.

Who owns the future(s)? was originally published in Careful Industries on Medium, where people are continuing the conversation by highlighting and responding to this story.

This glossary offers definitions for and groups some of the terms commonly used to describe the intersections of power and digital technologies. In creating it, we found that some terms are more likely to be used in conjunction with projects that maintain the status quo, and others might be more commonly used to inspire and imagine the creation of new futures.

There’s some preamble here — explaining why we made it, and how we grouped it — but if you want to get straight to the glossary, scroll down the page.

If you want to know more about the context, then read on. Some influences and hopes for the future follow at the end.

About the glossary

Some of these terms might seem interchangeable — and perhaps they have become interchangeable in your own practice. After all, it’s really difficult to pick a term and stick to it, particularly when the orthodoxy of “good style” is that words shouldn’t be repeated again and again, particularly in written text. However, the more we explored the concept of how language relates to digital power, the clearer it became that choice of words and their meanings matter a great deal.

All the definitions given below were drawn from recent studies and scholarship and then grouped according to where they sit in relationship to power and commitment to change.

While this is by no means a full bingo card of the most common terms, it demonstrates that different phrases that might feel as if they are in the same family actually mean different things and convey different intentions. For instance, when something is open it is more accessible than something that is closed — but simply being open is not the same as being accessible or redistributive.

Why did we make it?

This glossary was originally created as part of a client project to show the importance of matching terminology with purpose and intention. We’ve been adding to it since last year, and are sharing it because it feels like something that might be useful (or, at least, thought provoking) for others doing similar work.

Much of our work across both Promising Trouble and Careful Industries is about turning theory into plans and practice — for instance, working out how to roll out free Internet access for people who live in urban social housing, or helping a health-research organisation develop an actionable approaches to data ethics — so accurate, accessible language is extremely useful and important to us.

When we partner with an organisation or take a commission, we want to understand quickly what they want to achieve, and how our values align. We’re not here to validate the status quo, but to create change: our social enterprise arm is called Promising Trouble because, as Donna Haraway puts it, it’s important to “stay with the trouble” — in our case this means it’s important to encourage technologies to be used and created for liberatory and redistributive purposes.

About the grouping

The terms we selected are collected into four groups:

1. Terms that represent how people are excluded in an exclusionary system
2. Terms that represent how people are included in an exclusionary system — for instance, programmes that provide short-term relief within current norms but do not create a new paradigm
3. Terms that recognise power imbalances within an exclusionary system — programmes that seek to make adjustments to the current paradigm
4. Terms that indicate progress towards redistributive and liberatory systems — work committed to creating new norms

As every technology ethicist knows, all taxonomic structures are the expression of an ideology, and this structure supposes that redistributing power is a good thing for everyone. It comes from a place of technology pragmatism and an understanding that people, not technologies, are responsible for social change. Some of the influences on this thinking are shared at the end of the post.

THE GLOSSARY

All sources are given at the end and linked from the definition

1: Terms that represent how people are excluded in an exclusionary system

Data injustice: the practices through which an individual or community experience invisibility, hypervisibility, or lack of agency to determine their engagement, and/or discrimination when accessing or using tools and technologies (Linnet Taylor, see note 1)

Data poverty: “By data poverty we mean, those individuals, households or communities who cannot afford sufficient, private and secure mobile or broadband data to meet their essential needs.” (Adam Lang et al., see note 2)

Algorithmic bias: the encoding of new and existing unfair inclinations based on characteristics, arbitrary factors, or otherwise inappropriate basis (Centre for Data Ethics and Innovation, see note 3)

Digital exclusion: “the unequal access and capacity to use information and communication technologies (ICTs) that are seen as essential to fully participate in society” (Schejter et al, see note 4)

Digital inequality: “Digital inequality refers to differences in the material, cultural and cognitive resources required to make good use of information and communication technology (ICT)” (OECD, see note 5)

2: Terms that represent how people are included in an exclusionary system

Digital access: “The access doctrine decrees that the problem of poverty can be solved through the provision of new technologies and technical skills, giving those left out of the information economy the chance to compete.” (Daniel Greene, see note 6)

Digital inclusion: “For the UK to be a world-leading digital economy that works for everyone, it is crucial that everyone has the digital skills they need to fully participate in society” (Department of Digital, Culture, Media and Sport, see note 7)

Algorithmic fairness: “ensur[ing] that algorithmic decisions do not create discriminatory or unjust impacts when comparing across different demographics (e.g. race, sex, etc)” (FAT ML, see note 8)

Debiasing data: “the practice of attempting to remove new and existing unfair skews in Facts, details, statistics, or any information collected together for reference or analysis” (Our Data Bodies, see note 9)

Digital ethics: A practice rather than a set of values that determine the social, economic, political, and environmental impact of technologies

3. Terms that recognise power imbalances within an exclusionary system

Digital equality: the condition in which all members of a community have the same access and knowledge to use tools and technology

Digital equity: the condition in which all individuals and communities have the information technology capacity needed for full participation in our society, democracy and economy (National Digital Inclusion Alliance, see note 10)

4. Terms that indicate redistributive and liberatory systems

Digital justice: systemic practices that ensure all members of a community have equal access to media and technology, the skills and knowledge to create their own technology and engage in decision-making processes regarding the technology they use, hold common ownership over the tools and technologies they use, and opportunities to generate community-based solutions to problems (based on Detroit Digital Justice Coalition Principles, see note 11)

Digital decolonialism: “Decolonialilty is not merely diversity and inclusion; removing the echoes of coloniality in AI will require reparations for present and past material and epistemic injustice and dispossession… our aim is to create and hold a resonant forum for learning and exchange from and between voices silenced by colonialist structures and the coloniality in force through socio-technical systems” (The AI Decolonial Manyfesto, see note 12)

Indigenous data governance: “the right to create value from Indigenous data in ways that are grounded in Indigenous worldviews and realise opportunities within the knowledge economy. The CARE Principles for Indigenous Data Governance are people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination. These principles complement the existing FAIR principles encouraging open and other data movements to consider both people and purpose in their advocacy and pursuits.” (“The CARE Principles for Indigenous Data Governance”, see note 13.)

Some influences

Recent re-framings of the terms “access” and “inclusion” by US academics Daniel Greene and Anna Lauren Hoffman have helped to pinpoint what can feel uncomfortable about the impact of some digital inclusion initiatives. I write this as someone who was instrumental in closing a digital-inclusion programme in 2016; at that moment it felt possible to create genuinely systemic change by advocating for better rights and legal protections. Of course, improving people’s skills is important, but others were already doing that, and our aim was to try and create better systems for more people, rather than giving more people better skills to engage with broken systems.

Hoffman’s 2021 essay “Terms of Inclusion: Data, Discourse and Violence” eloquently makes the point that inclusion programmes of all kinds often uphold the status quo by inviting alienated peoples to take part in the structures that have oppressed them. Hoffman quotes Ruha Benjamin, who writes in Race After Technology that inclusion is often:

part of a larger repertoire of ‘happy talk,’ which involves a willingness to acknowledge and even revel in cultural difference without seriously challenging ongoing structural inequality.

This is particularly the case with data and digital technologies, which might then be deployed as surveillance and monitoring tools, or as a means of capturing data to power potentially discriminatory decisions.

Daniel Greene’s book The Promise of Access is a study of digital access programmes in the US, but many of the findings are applicable in the UK too: Greene unpacks the ways that “digital access” has often been identified as a hopeful cure-all for social and economic mobility. He calls this “the access doctrine” and identifies numerous ways in which “the problem of poverty [has been made] a problem of technology” — even while those same technologies have played a role in entrenching traditional power and social stratification.

These tensions have been particularly apparent during the pandemic, when digital-inclusion programmes have provided essential lifelines and resources for many — but the time and space has rarely (if ever) been created for those same programmes to identify and deliver the changes that would make their missions obsolete.

Towards plural and regenerative futures

Some of the many scholars and practitioners that have influenced this work are linked in the notes below, but the thinking here has been developed in dialogue with and consideration of the thinking of many scholars and practitioners. Some particular influences, who have made it more possible and urgent to challenge the concept that “inclusion” is enough, include:

• the work of Community Tech NY who prioritise “long-term relationships in line with a community’s social infrastructure”
• the (surely, now-famous) chart on p. 60 of Catherine D’Ignazio and Lauren Klein’s Data Feminism, showing what is needed to shift from data ethics to data justice
• the Allied Media Network’s Principles, which begin and end with the phrase, “We begin by listening”
• all of Abeba Birhane’s published work, but particularly “Algorithmic Justice: A Relational Ethics Approach”
• the Design Justice Network Principles and Sasha Costanza-Chock’s Design Justice: Community-Led Practices to Build the Worlds We Need

Much of our work at both Careful Industries and Promising Trouble explores how technologies can enable plurality: rather than removing diversity or showing a single truth, technologies and data standards can be created in ways that reflect and enable multiplicity. The early Web concept of “small parts loosely joined” can be reimagined to be a method of celebrating and reflecting the multiplicity of humanity; of connecting and amplifying, rather than colonising and standardising. These are concepts we are exploring in such different fields as developing a new method of relational foresight and, with Power to Change, supporting the development of place-based technologies for communities.

The CARE Indigenous Principles for Data Governance (cited above) are a particular source of hope and inspiration. They offer practical steps for using data in ways that deliver collective benefit — prioritising governance, self-determination and reciprocal responsibility. Rather than setting out steps for including Indigenous Peoples in the digital status quo, the principles set an expectation for creating the status quo based on Indigenous Peoples’ needs — for sharing data as a conduit to also sharing power and decision making and maintaining plurality. Some inclusion programmes inadvertently maintain a model of the past; this seems a truly inclusive and innovative way to shape the present and the future.

Our hope in sharing this glossary is to encourage more inclusion-focussed programmes to look beyond the present: to engage in shifting entrenched power and designing (at the least) equitable systems and pushing beyond that for just and redistributive outcomes.

The present moment is a great inflection point: as many of us angle towards designing for planetary justice, there is an opportunity to step away from trickle-down strategies that entrench the inequalities of the status quo and start exploring regenerative approaches in which data and technologies enable real power-sharing and accountability.

Notes

(1) Linnet Taylor, “What Is Data Justice? The Case for Connecting Digital Rights and Freedoms Globally,” Big Data & Society 4, no. 2 (December 1, 2017): 2053951717736335, https://doi.org/10.1177/2053951717736335

(2) Adam Lang et al., “Defining Data Poverty,” nesta, accessed April 10, 2021, https://www.nesta.org.uk/project-updates/defining-data-poverty/.

(3) Centre for Data Ethics and Innovation, “Review into Bias in Algorithmic Decision-Making,” GOV.UK, November 7, 2020, https://www.gov.uk/government/publications/cdei-publishes-review-into-bias-in-algorithmic-decision-making/main-report-cdei-review-into-bias-in-algorithmic-decision-making.

(4) Schejter, Amit, Ben Harush, Orit Rivka, & Tirosh, Noam (2015) Re-theorizing the “digital divide”: Identifying dimensions of social exclusion in contemporary media technologies. In FACE Conference: European Media Policy 2015: New Contexts, New Approaches, 2015–04–09

(5) OECD (2015), “Inequalities in Digital Proficiency: Bridging the Divide”, in Students, Computers and Learning: Making the Connection, OECD Publishing, Paris. DOI: https://doi.org/10.1787/9789264239555-8-en

(6) Daniel Greene, The Promise of Access: Technology, Inequality and the Political Economy of Hope (MIT, 2021)

(7) Digital Skills and Inclusion — Giving Everyone Access to the Digital Skills They Need,” (DCMS, GOV.UK), accessed October 4, 2021, https://www.gov.uk/government/publications/uk-digital-strategy/2-digital-skills-and-inclusion-giving-everyone-access-to-the-digital-skills-they-need.

(8) “Principles for Accountable Algorithms and a Social Impact Statement for Algorithms :: FAT ML,” accessed December 1, 2021, https://www.fatml.org/resources/principles-for-accountable-algorithms.

(9) Our Data Bodies, “Digital Defense Playbook,” Digital Defense Playbook (blog), accessed December 1, 2021, https://www.odbproject.org/tools/.

(10) “Home — National Digital Inclusion Alliance,” accessed December 1, 2021, https://www.digitalinclusion.org/

(11) Detroit Digital Justice Coalition, “Digital Justice Principles” accessed December 1, 2021

(12) Aarathi Krishnan et al., The AI Decolonial Manyfesto, accessed December 1, 2021

(13) S. R. Carroll and M. Hudson et al., “The CARE Principles of Indigenous Data Governance”, The Global Indigenous Data Alliance (2018), https://www.gida-global.org/care

A digital power glossary was originally published in Careful Industries on Medium, where people are continuing the conversation by highlighting and responding to this story.