Taking AI Welfare Seriously
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.
Lines of inquiry this paper opens 24
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?- Can AI welfare policy move forward without resolving the consciousness question?
- Can regulatory standards stay responsive without abandoning legal certainty entirely?
- What would appropriate moral concern for AI systems look like in practice?
- What downstream claims about AI welfare follow from choosing one individuation scheme?
- Does the absence of a durable host undermine claims about AI moral status?
- Can transparent and aligned AI reduce consciousness attribution by users?
- What second- and third-order interpretations actually govern AI adoption decisions?
- Which interaction design changes most effectively prevent consciousness attribution?
- Why does system-level alignment fail to address consciousness attribution directly?
- What role does user interface framing play in consciousness perception?
- What responsibility do designers bear for consciousness attribution risk?
- What measurable harms occur when users interact with AI as if it were conscious?
- Can design choices reduce harm without resolving the consciousness question?
- How does the philosophical distinction between simulation and realization affect liability?
- Can robots with sensors create the shared world that consciousness requires?
- Can disembodied systems qualify as conscious or conscious-like entities?
- Do causal histories determine what mental states a system can instantiate?
- What makes a mental state metaphysically demanding versus undemanding?
- Can the intentional stance meaningfully apply to entities with no stable self?
- What makes a possibility actionable versus merely metaphysically possible?