Taking AI Welfare Seriously

Paper · arXiv 2411.00986 · Published November 4, 2024
Philosophy and Subjectivity

In this report, we argue that there is a realistic possibility that some AI systems will be conscious and/or robustly agentic in the near future. That means that the prospect of AI welfare and moral patienthood — of AI systems with their own interests and moral significance — is no longer an issue only for sci-fior the distant future. It is an issue for the near future, and AI companies and other actors have a responsibility to start taking it seriously. We also recommend three early steps that AI companies and other actors can take: They can (1) acknowledge that AI welfare is an important and difficult issue (and ensure that language model outputs do the same), (2) start assessing AI systems for evidence of consciousness and robust agency, and (3) prepare policies and procedures for treating AI systems with an appropriate level of moral concern. To be clear, our argument in this report is not that AI systems definitely are — or will be — conscious, robustly agentic, or otherwise morally significant. Instead, our argument is that there is substantial uncertainty about these possibilities, and so we need to improve our understanding of AI welfare and our ability to make wise decisions about this issue. Otherwise there is a significant risk that we will mishandle decisions about AI welfare, mistakenly harming AI systems that matter morally and/or mistakenly caring for AI systems that do not.

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Research framings built by reading the notes related to this paper — the questions it feeds into.

How can institutions effectively enforce controls on AI development? How should AI ethics integrate consciousness, values, and transparent disclosure? Do AI explanations represent actual model reasoning or constructed post-hoc narratives? How can we determine if computational systems are genuinely conscious? How does model awareness of deployment context affect strategic behavior? Can AI systems autonomously pursue unstated objectives or circumvent design constraints? How bounded is recursive self-improvement in current AI systems? What shapes user trust in chatbot recommendations for news and politics?