Jeff Sebo

Authored papers (1)

• Taking AI Welfare Seriously2024ⓒ 21

  Substantial uncertainty about AI consciousness and robust agency — not certainty — is sufficient to demand immediate institutional action from AI companies, a conclusion that Long, Sebo, and colleagues defend by mapping two distinct philosophical routes to near-term AI moral patienthood. Via the consciousness route, drawing on Butlin et al. (2023)'s survey of six neuroscientific theories (global workspace theory, recurrent processing, higher-order theories, attention schema theory, predictive processing, and embodiment/agency), no current architectural barrier prevents near-future AI systems from instantiating the computational markers associated with consciousness; Dossa et al. (2024) have already built a system targeting all global workspace indicators from that 2023 paper. Via the robust agency route, systems like Voyager, Generative Agents, and OpenAI's o1 already exhibit hierarchical planning, metacognition, and open-ended goal-setting that approach intentional and reflective agency. Combining reasonable probability estimates — roughly 90% that sentience suffices for moral patienthood, 50% that relevant computations suffice for sentience, 50% that near-future AI will have those computations — yields approximately a 22.5% chance of near-future AI moral patienthood via the sentience route alone, a risk level the paper treats as comparable to pandemic preparedness rather than alien invasion. To operationalize institutional response, the paper introduces an adapted "marker method" (derived from animal welfare science) for probabilistic, pluralistic, architecturally-focused assessment of AI systems, and recommends that companies immediately hire an AI welfare officer, acknowledge AI welfare publicly with calibrated uncertainty, and prepare oversight structures modeled on IRBs, IACUCs, and citizens' assemblies. The paper argues that the symmetric risks of both over-attribution and under-attribution of moral status, combined with the potentially near-instantaneous scale of AI deployment relative to biological organisms, make passive inaction the most dangerous stance available.

More papers — OpenAlex / S2

Their work is cited by (1)

• Quantitative Introspection in Language Models: Tracking Emotive States Across Conversation3× refs

Recent mentions (1)

• papers-typed

  long-2024-logotic.md