One response to the existential threat posed by a super-intelligent AI is to design it to be friendly to us. Some have argued that even if this were possible, the resulting AI would treat us as we do our pets. Sparrow (AI & Soc. https://doi.org/10.1007/s00146-023-01698-x, 2023) argues that this would be a bad outcome, for such an AI would dominate us—resulting in our freedom being diminished (Pettit in Just freedom: A moral compass for a complex world. WW Norton & Company, (...) 2014). In this paper, I consider whether this would be such a bad outcome. (shrink)

The notion of existential catastrophe is increasingly appealed to in discussion of risk management around emerging technologies, but it is not completely clear what this notion amounts to. Here, I provide an opinionated survey of the space of plausibly useful definitions of existential catastrophe. Inter alia, I discuss: whether to define existential catastrophe in ex post or ex ante terms, whether an ex ante definition should be in terms of loss of expected value or loss of potential, and what kind (...) of probabilities should be involved in any appeal to expected value. (shrink)

Recent advances in natural language processing have given rise to a new kind of AI architecture: the language agent. By repeatedly calling an LLM to perform a variety of cognitive tasks, language agents are able to function autonomously to pursue goals specified in natural language and stored in a human-readable format. Because of their architecture, language agents exhibit behavior that is predictable according to the laws of folk psychology: they function as though they have desires and beliefs, and then make (...) and update plans to pursue their desires given their beliefs. We argue that the rise of language agents significantly reduces the probability of an existential catastrophe due to loss of control over an AGI. This is because the probability of such an existential catastrophe is proportional to the difficulty of aligning AGI systems, and language agents significantly reduce that difficulty. In particular, language agents help to resolve three important issues related to aligning AIs: reward misspecification, goal misgeneralization, and uninterpretability. (shrink)

When asked about humanity’s future relationship with computers, Marvin Minsky famously replied “If we’re lucky, they might decide to keep us as pets”. A number of eminent authorities continue to argue that there is a real danger that “super-intelligent” machines will enslave—perhaps even destroy—humanity. One might think that it would swiftly follow that we should abandon the pursuit of AI. Instead, most of those who purport to be concerned about the existential threat posed by AI default to worrying about what (...) they call the “Friendly AI problem”. Roughly speaking this is the question of how we might ensure that the AI that will develop from the first AI that we create will remain sympathetic to humanity and continue to serve, or at least take account of, our interests. In this paper I draw on the “neo-republican” philosophy of Philip Pettit to argue that solving the Friendly AI problem would not change the fact that the advent of super-intelligent AI would be disastrous for humanity by virtue of rendering us the slaves of machines. A key insight of the republican tradition is that freedom requires equality of a certain sort, which is clearly lacking between pets and their owners. Benevolence is not enough. As long as AI has the power to interfere in humanity’s choices, and the capacity to do so without reference to our interests, then it will dominate us and thereby render us unfree. The pets of kind owners are still pets, which is not a status which humanity should embrace. If we really think that there is a risk that research on AI will lead to the emergence of a superintelligence, then we need to think again about the wisdom of researching AI at all. (shrink)

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, (...) we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

Catastrophic risk raises questions that are not only of practical importance, but also of great philosophical interest, such as how to define catastrophe and what distinguishes catastrophic outcomes from non-catastrophic ones. Catastrophic risk also raises questions about how to rationally respond to such risks. How to rationally respond arguably partly depends on the severity of the uncertainty, for instance, whether quantitative probabilistic information is available, or whether only comparative likelihood information is available, or neither type of information. Finally, catastrophic risk (...) raises important ethical questions about what to do when catastrophe avoidance conflicts with equity promotion. (shrink)

Organizations that develop and deploy artificial intelligence (AI) systems need to manage the associated risks—for economic, legal, and ethical reasons. However, it is not always clear who is responsible for AI risk management. The three lines of defense (3LoD) model, which is considered best practice in many industries, might offer a solution. It is a risk management framework that helps organizations to assign and coordinate risk management roles and responsibilities. In this article, I suggest ways in which AI companies could (...) implement the model. I also discuss how the model could help reduce risks from AI: it could identify and close gaps in risk coverage, increase the effectiveness of risk management practices, and enable the board of directors to oversee management more effectively. The article is intended to inform decision-makers at leading AI companies, regulators, and standard-setting bodies. (shrink)