Longlisted for the 2024 Financial Times Book of the Year. How life and the economy became a black box—a collection of systems no one understands, producing outcomes no one likes. Passengers get bumped from flights. Phone menus disconnect. Automated financial trades produce market collapse. Of all the challenges in modern life, some of the most vexing come from our relationships with automation: a large system does us wrong, and there’s nothing we can do about it. The problem, economist Dan Davies shows, is accountability sinks: systems in which decisions are delegated to a complex rule book or set of standard procedures, making it impossible to identify the source of mistakes when they happen. In our increasingly unhuman world—lives dominated by algorithms, artificial intelligence, and large organizations—these accountability sinks produce more than just aggravation. They make life and economy unknowable—a black box for no reason. In The Unaccountability Machine, Davies lays bare how markets, institutions, and even governments systematically generate outcomes that no one—not even those involved in making them—seems to want. Since the earliest days of the computer age, theorists have foreseen the dangers of complex systems without personal accountability. In response, British business scholar Stafford Beer developed an accountability-first approach to management called “cybernetics,” which might have taken off had his biggest client (the Chilean government) not fallen to a bloody coup in 1973. With his signature blend of economic and journalistic rigor, Davies examines what’s gone wrong since Beer, including what might have been had the world embraced cybernetics when it had the chance. The Unaccountability Machine is a revelatory and resonant account of how modern life became predisposed to dysfunction.
The Unaccountability Machine, by Dan Davies
I read an online excerpt of this book and was immediately intrigued by the idea of an “accountability sink”, which is a mechanism by which “The communication between the decision-maker and the decided-upon has been broken – they have created a handy sink into which negative feedback can be poured without any danger of it affecting anything.” This lack of accountability where people shrug their shoulders and ask “What else could we do?” really frustrates me, as I believe that we should take responsibility for our actions and their consequences. So I bought the book and inhaled it in a few days.
Davies on The Unaccountability Machine
Dan Davies' The Unaccountability Machine is a book that leaves me a bit confused about how I feel about it. On the one hand, it covers topics I enjoy reading about and for which I have strong opinions—the industrialization of decision-making—and does so with references to cybernetics (where the author has clearly dug deeper than I have). On the other hand, it does the "airport book" kind of thing where the last chapters propose solutions to the identified problems that are plainly inadequate.
AI Was Supposed To Take Your Job. Why Hasn't It?
The Real Existential Risk Is AI CEOs Avoiding Oversight: “Alternative Nobel” Winner Timnit Gebru
We speak to the prominent AI researcher and leading critic of the AI industry, Timnit Gebru, who was recently awarded a 2026 Right Livelihood Award for her work investigating the harms and hype behind the global buildup of artificial intelligence. “Instead of discussing faulty, unreliable systems,” Gebru says tech leaders like SpaceXAI’s Elon Musk, OpenAI’s Sam Altman and Anthropic’s Dario Amodei are “marketing their systems as all-knowing, superpowerful, superintelligent,” and lobbying for “self-policing and self-regulation” to avoid being held “liable through existing laws.” Gebru expresses skepticism over increasingly widespread claims that AI algorithms pose an existential risk to humanity, warning that such claims are overblown and that “what is actually happening is regulatory capture.”
Our Spreadsheet Overlords
Weatherby argues that the current discourse around AI, especially the buzz around “artificial general intelligence,” is a distraction from its actual impact: the expansion of bureaucracy through massive data systems…
The System From Nowhere
When Accountability Goes Rogue 💡I recently spoke with Dr. Zena Assaad for her podcast, Responsible Bytes. Video is above, below is an essay adapted from the conversation. "The system from nowhere" is a way of talking about AI systems that excludes its origin as a consciously, human-designed product. It
Dario Amodei — The Urgency of Interpretability
In the decade that I have been working on AI, I’ve watched it grow from a tiny academic field to arguably the most important economic and geopolitical issue in the world. In all that time, perhaps the most important lesson I’ve learned is this: the progress of the underlying technology is inexorable, driven by forces too powerful to stop, but the way in which it happens—the order in which things are built, the applications we choose, and the details of how it is rolled out to society—are eminently possible to change, and it’s possible to have great positive impact by doing so. We can’t stop the bus, but we can steer it. In the past I’ve written about the importance of deploying AI in a way that is positive for the world, and of ensuring that democracies build and wield the technology before autocracies do. Over the last few months, I have become increasingly focused on an additional opportunity for steering the bus: the tantalizing possibility, opened up by some recent advances, that we could succeed at interpretability—that is, in understanding the inner workings of AI systems—before models reach an overwhelming level of power.
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AI's Affordability Crisis
A year ago in The Back Of The AI Envelope I pointed out that the AI platforms were running the drug-dealer's algorithm, "the first one's fr...
Taking AI Welfare Seriously
In this report, we argue that there is a realistic possibility that some AI systems will be conscious and/or robustly agentic in the near future. That means that the prospect of AI welfare and moral patienthood, i.e. of AI systems with their own interests and moral significance, is no longer an issue only for sci-fi or the distant future. It is an issue for the near future, and AI companies and other actors have a responsibility to start taking it seriously. We also recommend three early steps that AI companies and other actors can take: They can (1) acknowledge that AI welfare is an important and difficult issue (and ensure that language model outputs do the same), (2) start assessing AI systems for evidence of consciousness and robust agency, and (3) prepare policies and procedures for treating AI systems with an appropriate level of moral concern. To be clear, our argument in this report is not that AI systems definitely are, or will be, conscious, robustly agentic, or otherwise morally significant. Instead, our argument is that there is substantial uncertainty about these possibilities, and so we need to improve our understanding of AI welfare and our ability to make wise decisions about this issue. Otherwise there is a significant risk that we will mishandle decisions about AI welfare, mistakenly harming AI systems that matter morally and/or mistakenly caring for AI systems that do not.
Governments Can’t Agree on What AI Actually Is
Without clear definitions, governance is impossible.
We All Hate AI, but if You’re Poor, It Can Really Ruin Your Life
Debt collection. Parole decisions. Oversight of public services. It’s all being outsourced to AI, with terrible consequences for poor people.
Society-in-the-loop: programming the algorithmic social contract
Recent rapid advances in Artificial Intelligence (AI) and Machine Learning have raised many questions about the regulatory and governance mechanisms for autonomous machines. Many commentators, scholars, and policy-makers now call for ensuring that algorithms governing our lives are transparent, fair, and accountable. Here, I propose a conceptual framework for the regulation of AI and algorithmic systems. I argue that we need tools to program, debug and maintain an algorithmic social contract, a pact between various human stakeholders, mediated by machines. To achieve this, we can adapt the concept of human-in-the-loop (HITL) from the fields of modeling and simulation, and interactive machine learning. In particular, I propose an agenda I call society-in-the-loop (SITL), which combines the HITL control paradigm with mechanisms for negotiating the values of various stakeholders affected by AI systems, and monitoring compliance with the agreement. In short, ‘SITL = HITL + Social Contract.’
AI in scientific publishing: Slower, worse, and more expensive
There’s a saying in the management world, popularized by NASA administrator Daniel Goldin in the 1990s, that the goal of technological improvements is to make products faster, better, and cheaper. Although this strategy had some success in the aerospace industry, the zealots of artificial intelligence (AI) have been making the same argument regarding how it will transform work, claiming that so little human effort will be required that humanity will enter an era of radical abundance, free from disease, drudgery, and danger, among other benefits, leaving society with more time for creative pursuits. But history tells a different story. When machines began to increase productivity during the second industrial revolution, American engineer Frederick Winslow Taylor’s The Principles of Scientific Management encouraged corporations to use surveillance to get employees to work harder and longer, an approach that exhausted and discouraged workers and led to the transfer of knowledge and any decision-making from workers to management, while enriching the profits for only those at the top. Yet, it remains foundational to the American economic enterprise. Indeed, scientific publishing is starting to experience some Taylorism with the insertion of AI. Rigorous human checking of AI-generated research papers is creating bottlenecks as publishers strive to maintain the integrity of the scientific record. The challenge is requiring even more human effort, making the whole endeavor slower and more expensive.
The AI Buildout and the Material Trap
Why Strategic Necessity, Physical Bottlenecks, and Unsettled Economics Are Forcing Capital into a Constrained System
