The algorithm and its discontents

In a new lawsuit in the US against Meta, 41 states and the District of Columbia argue that two of the company’s social-media products – Instagram and Facebook – are not just addictive but detrimental to children’s well-being. Meta is accused of engaging in a “scheme to exploit young users for profit,” including by showing harmful content that keeps them glued to their screens.
According to one recent poll, 17-year-olds in the US spend 5.8 hours per day on social media. How did it come to this? The answer, in a word, is “engagement.”
Deploying algorithms to maximise user engagement is how Big Tech maximises shareholder value, with short-term profits often overriding longer-term business objectives, not to mention societal health. As the data scientist Greg Linden puts it, algorithms built on “bad metrics” foster “bad incentives” and enable “bad actors.”
Although Facebook started as a basic service that connected friends and acquaintances online, its design gradually evolved not to meet user needs and preferences, but to keep them on the platform and away from others. In pursuit of this objective, the company regularly disregarded explicit consumer preferences regarding the kind of content users wanted to see, their privacy, and data sharing.
Putting immediate profits first means funnelling users toward “clicks,” even though this approach generally favours inferior, sensational material, rather than fairly rewarding participants from across a broader ecosystem of content creators, users, and advertisers. We call these profits “algorithmic attention rents,” because they are generated by passive ownership (like a landlord) rather than from entrepreneurial production to meet consumers’ needs.
Mapping rents in today’s economy requires understanding how dominant platforms exploit their algorithmic control over users. When an algorithm degrades the quality of the content it promotes, it is exploiting users’ trust and the dominant position that network effects reinforce. That is why Facebook, Twitter, and Instagram can get away with cramming their feeds with ads and “recommended” addictive content.
The Meta suit is ultimately about its algorithmic practices that are carefully constructed to maximise user “engagement” – keeping users on the platform for longer and provoking more comments, likes, and reposts. Often, a good way to do this is to display harmful and borderline illegal content, and to transform time on the platform into a compulsive activity, with features like “infinite scroll” and non-stop notifications and alerts (many of the same techniques are used, to great effect, by the gambling industry).
Now that advances in artificial intelligence already supercharge algorithmic recommendations, making them even more addictive, there is an urgent need for new governance structures oriented toward the “common good” (rather than a narrowly conceived notion of “shareholder value”) and symbiotic partnerships between business, government, and civil society. Fortunately, it is well within policymakers’ power to shape these markets for the better.
First, rather than relying only on competition and antitrust law, policymakers should adopt technological tools to ensure that platforms cannot unfairly lock in users and developers. One way to prevent anti-competitive “walled gardens” is by mandating data portability and interoperability across digital services, so that users can move more seamlessly between platforms, depending on where their needs and preferences are best met.
Second, corporate governance reform is essential, since maximisation of shareholder value is what pushed platforms to exploit their users algorithmically in the first place. Given the well-known social costs associated with this business model – optimising for clicks often means amplifying scams, misinformation, and politically polarising material – governance reform requires algorithmic reform.
Third, users should be given greater influence over the algorithmic prioritisation of information shown to them. Otherwise, the harms from ignoring user preferences will continue to grow as algorithms create their own feedback loops, pushing manipulative clickbait on users and then wrongly inferring that they prefer it.
Fourth, the industry standard of “A/B testing” should give way to more comprehensive long-term impact evaluations. Faulty data science drives algorithmic short-termism.
Fifth, public AI should be deployed to evaluate the quality of algorithmic outputs, particularly advertising.
Meta’s forthcoming trial cannot undo past mistakes. But as we prepare for the next generation of AI products, we must establish proper algorithmic oversight. AI-powered algorithms will influence not just what we consume, but how we produce and create; not just what we choose, but what we think. We must not get this wrong.