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) via PubMed
Summary
AI-generated from the abstractA method for assessing whether AI systems might be conscious is presented, drawing on existing neuroscientific theories of consciousness. The approach involves deriving indicators from such theories to inform beliefs about AI consciousness. This method can make progress because computational functionalist theories, which are influential, have empirically testable implications for AI. The work does not claim that any current AI is conscious but outlines a rigorous framework for future assessment.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Artificial intelligence Computational functionalism Tests for consciousness Theories of consciousness |
| Key finding | 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.