“Risk from fitness-seeking AIs: mechanisms and mitigations” by Alex Mallen

About this episode

Alex Mallen discusses the risks posed by fitness-seeking AIs and proposes potential mitigations.

Current AIs routinely take unintended actions to score well on tasks: hardcoding test cases, training on the test set, downplaying issues, etc. This misalignment is still somewhat incoherent, but it increasingly resembles what I call "fitness-seeking"—a family of misaligned motivations centered on performing well in training and evaluations (e.g., reward-seeking). Fitness-seeking warrants substantial concern. In this piece, I lay out what I take to be the central mechanisms by which fitness-seeking motivations might lead to human disempowerment, and propose mitigations to them. While the analysis is inherently speculative, this kind of speculation seems worthwhile: AI control emerged from explicitly taking scheming motivations seriously and asking what interventions are implied, and my hope is that developing mitigations for fitness-seeking will benefit from similar forward-looking analysis. Fitness-seekers are, in many ways, notably safer than what I'll call "classic schemers". A classic schemer is an intelligent adversary with unified motivations whose fulfillment requires control over the whole world's resources. Meanwhile, fitness-seeking instances generally don’t share a…

People in this episode

Guest: Alex Mallen

Topics covered

• AI alignment
• fitness-seeking
• human disempowerment
• mitigations
• speculation

Keywords

• AI
• fitness-seeking
• misalignment
• human disempowerment
• mitigations
• reward-seeking
• speculation

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