Upcoming Event

The Fair Treatment of Unequal Claims: The Moral Importance of Injecting Randomness into Algorithmic Decision Systems

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

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A Matter of Principle? AI alignment as the Fair Treatment of Claims (co-authored with Geoffrey Keeling)

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

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Evaluating AI Agents for Dangerous (Cognitive) Capabilities

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,

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What remains of the singularity hypothesis?

Abstract: The idea that advances in the cognitive capacities of foundation models like LLMs will lead to a period of rapid, recursive self-improvement — an “intelligence explosion” or “technological singularity” — has recently come under sustained criticism by academic philosophers. I evaluate the extent to which this criticism successfully undermines the argument for a singularity,

What remains of the singularity hypothesis? Read More »

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