Does the assumption of a weak form of computational functionalism, according to which the right form of neural computation is sufficient for consciousness, entail that a digital computational simulation of such neural computations is conscious? Or must this computational simulation be implemented in the right way, in order to replicate consciousness? From the perspective of Karl Friston’s free energy principle, self-organising systems (such as living organisms) share a set of properties that could be realised in artificial systems, but are not (...) instantiated by computers with a classical (von Neumann) architecture. I argue that at least one of these properties, viz. a certain kind of causal flow, can be used to draw a distinction between systems that merely simulate, and those that actually replicate consciousness. (shrink)

Some people believe that advanced artificial intelligence systems (AIs) might, in the future, come to have moral status. Further, humans might be tempted to design such AIs that they serve us, carrying out tasks that make our lives better. This raises the question of whether designing AIs with moral status to be willing servants would problematically violate their autonomy. In this paper, I argue that it would in fact do so.

A central question in AI ethics concerns the moral status of robots. This article argues against the idea that they have moral status. It proceeds by defending the assumption that consciousness is necessary for welfare subjectivity. Since robots most likely lack consciousness, and welfare subjectivity is necessary for moral status, it follows that robots lack moral status. The assumption that consciousness is necessary for welfare subjectivity appears to be in tension with certain widely accepted theories of wellbeing, especially versions of (...) Desire Satisfaction Theory and Objective List Theory. However, instead of elevating non-conscious robots to welfare subjects, this tension should lead us to reject versions of these theories that have this implausible implication. (shrink)

While most contemporary proposals of ethics for machines draw upon principle-based ethics, a number of recent studies attempt to build machines capable of acting virtuously. This paper discusses the promises and limitations of building virtue-ethical machines. Taking inspiration from various philosophical traditions—including Greek philosophy (Aristotle), Chinese philosophy (Zhuangzi), phenomenology (Hubert and Stuart Dreyfus) and contemporary virtue theory (Julia Annas)—we argue for a novel model of machine ethics we call the “skillful-expert model.” This model sharply distinguishes human virtues and their machine (...) analogues and rejects the direct transposition of human virtues to machines, as suggested by most proposals for machine virtues. Instead, we propose that human skills are better candidates for machine virtues. (shrink)

The normative challenge of AI alignment centres upon what goals or values ought to be encoded in AI systems to govern their behaviour. A number of answers have been proposed, including the notion that AI must be aligned with human intentions or that it should aim to be helpful, honest and harmless. Nonetheless, both accounts suffer from critical weaknesses. On the one hand, they are incomplete: neither specification provides adequate guidance to AI systems, deployed across various domains with multiple parties. (...) On the other hand, the justification for these approaches is questionable and, we argue, of the wrong kind. More specifically, neither approach takes seriously the need to justify the operation of AI systems to those affected by their actions – or what this means for pluralistic societies where people have different underlying beliefs about value. To address these limitations, we propose an alternative account of AI alignment that focuses on fair processes. We argue that principles that are the product of these processes are the appropriate target for alignment. This approach can meet the necessary standard of public justification, generate a fuller set of principles for AI that are sensitive to variation in context, and has explanatory power insofar as it makes sense of our intuitions about AI systems and points to a number of hitherto underappreciated ways in which an AI system may cease to be aligned. (shrink)