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Atlas / Digital minds / People

Robert Long

WelfareIntrospection & Self-report
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Eleos AI Research (Executive Director) · website

AI welfare, self-reports, safety–welfare interactions, practical interventions.

Placed by the editor, v1 — corrections welcome.

In the Atlas

  • Long Sebo et al 2024 - Taking AI welfare seriously
  • Binder et al 2025 - Looking inward
  • Butlin Long et al 2023 - Consciousness in AI
  • Butlin Long et al 2025 - Identifying indicators of consciousness
  • Perez Long 2023 - Self-reports and moral status
  • Long Sebo Sims 2025 - Tension between AI safety and AI welfare
  • Long 2025 - Why it makes sense to let Claude exit conversations
  • Sebo Long 2025 - Moral consideration for AI systems by 2030

Closest to

  • Counting moral patients
  • How well can LLMs introspect
  • What counts as welfare evidence
  • When self-report counts as welfare evidence
  • Safety and welfare tension
  • Cheap robust welfare interventions
  • What is owed at deprecation

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Around this researcher
Introspection & Self-reportWelfareLooking inward: language models can learn about themselves by introspectionConsciousness in artificial intelligence: insights from the science of consciousnessIdentifying indicators of consciousness in AI systemsConsciousness and cognitive access in LLMs: commentary on the global workspace paperWhy it makes sense to let Claude exit conversationsIs there a tension between AI safety and AI welfare?Taking AI welfare seriouslyThis researcher

19 connections · Explore on map ↗

Linked from

  • Introspection & Self-report
  • Welfare
  • Looking inward: language models can learn about themselves by introspection
  • Consciousness in artificial intelligence: insights from the science of consciousness
  • Identifying indicators of consciousness in AI systems
  • Consciousness and cognitive access in LLMs: commentary on the global workspace paper
  • Why it makes sense to let Claude exit conversations
  • Is there a tension between AI safety and AI welfare?
  • Taking AI welfare seriously
  • Studying AI Welfare Empirically
  • Towards evaluating AI systems for moral status using self-reports
  • Moral consideration for AI systems by 2030
  • Which welfare interventions are cheap and robust under deep uncertainty?
  • How do we count moral patients when one model runs millions of conversations?
  • How well can LLMs introspect — and how much does "just ask the model" tell us?
  • Is there a tension between AI safety and AI welfare?
  • What would count as evidence about an AI system's welfare?
  • What, if anything, is owed to a model at deprecation?
  • When does a model's self-report count as welfare evidence?

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