Identifying indicators of consciousness in AI systems.
Patrick Butlin, Robert Long, Tim Bayne, Yoshua Bengio, Jonathan Birch, David J. Chalmers, Axel Constant, George Deane, Eric Elmoznino, Stephen M Fleming, Xu Ji, Ryota Kanai, Colin Klein, Grace Lindsay, Matthias Michel, Liad Mudrik, Megan A. K. Peters, Eric Schwitzgebel, Jonathan Simon, Rufin Vanrullen
Trends in Cognitive Sciences June 1, 2026 DOI: 10.1016/j.tics.2025.10.011 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Artificial intelligence Computational functionalism Tests for consciousness Theories of consciousness |
| Key points | Proposes a method for assessing AI consciousness by deriving indicators from neuroscientific theories of consciousness, particularly computational functionalist theories, to inform credences about whether AI systems are conscious. |
Abstract
Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.