Upcoming Event

Abstract: With rapid advances in the social and cognitive capabilities of AI, questions concerning their consciousness and moral status are becoming increasingly pressing. In this talk, I contrast two radically different approaches—Deep and Shallow—to addressing these issues. Deep approaches dominate mainstream philosophy of mind and consciousness science, viewing consciousness as an empirically discoverable natural kind

Abstract: Around 40% of philosophers believe that future AI systems will be conscious (Bourget and Chalmers 2023). Others have argued that the realization of phenomenal consciousness in conventional computer hardware is infeasible, nomologically impossible, or even metaphysically impossible (Block 2009; Godfrey-Smith 2016; Searle 1992; Shiller 2024; Tononi and Koch 2015). Whether or not the AI systems

Abstract: Late in his career, Daniel Dennett changed his mind about minds. If his new view is correct, it has deep implications for both the moral standing of nascent AIs and for the existential danger they may pose. He suggests in his (2017) that genuine minds require a different kind of architecture than he previously

Abstract: Most agree that consciousness bears some sort of important connection to well-being. For example, consciousness is necessary for having experiences of pleasure and pain, and these experiences are often — if not always — good and bad for us, respectively. The subject of AI consciousness is therefore highly relevant to the subject of AI

Abstract: Much of the recent discourse on AI consciousness has centered around two questions: is artificial consciousness possible in principle and do current systems meet the requirements laid out by prominent theories. For many practical purposes, it matters more whether we are likely to see nontrivial numbers of conscious artificial minds as the technology matures.

Abstract: Identifying the presence of consciousness is difficult even in humans when one does not want to rely on subjective reports [1,2], but it is increasingly more difficult in animals [3,4], and, due to the lack of common evolutionary history, even more so in artificial systems [5,6]. Currently, there are two major alternatives on the

Abstract: One of the more promising empirical theories of phenomenal consciousness is the Global Workspace Theory (GWT), which says that a mental representation becomes conscious when it enters a global workspace from which it can be broadcast to various specialized psychological modules [1-3]. There has been some discussion of GWT in connection with recent advancements

Abstract: One compelling approach to value alignment treats value alignment as fundamentally a question of the fairness of the procedure for determining the principles that AI systems should align with (Gabriel 2020). This talk examines the question of how a procedure should treat different stakeholders’ claims fairly, especially in multi-stakeholder cases where people have both

Abstract: The normative challenge of AI alignment centres upon what goals or values to encode 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

Abstract: AI agents based on Large Language Models (LLMs) demonstrate human-level performance at some theory of mind (ToM) tasks (Kosinski 2024; Street et al. 2024). Here ToM is roughly the ability to predict and explain behaviour by attributing mental states to oneself and others. ToM capabilities matter for AI safety because, at least in humans,