Identifying indicators of consciousness in AI systems

Butlin, Patrick, Long, Robert, Bayne, Tim, Bengio, Yoshua, Birch, Jonathan ORCID: 0000-0001-7517-4759, Chalmers, David, Constant, Axel, Deane, George, Elmoznino, Eric, Fleming, Stephen M, Ji, Xu, Kanai, Ryota, Klein, Colin, Lindsay, Grace, Michel, Matthias, Mudrik, Liad, Peters, Megan A K, Schwitzgebel, Eric, Simon, Jonathan and VanRullen, Rufin (2025) Identifying indicators of consciousness in AI systems. Trends in Cognitive Sciences. ISSN 1364-6613 (In Press)

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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. [Abstract copyright: Copyright © 2025 The Authors. Published by Elsevier Ltd.. All rights reserved.]

Item Type:	Article
Additional Information:	© 2025 The Authors
Divisions:	Philosophy, Logic and Scientific Method
Subjects:	Q Science > Q Science (General) B Philosophy. Psychology. Religion > B Philosophy (General)
Date Deposited:	25 Nov 2025 16:33
Last Modified:	03 Dec 2025 08:58
URI:	http://eprints.lse.ac.uk/id/eprint/130322

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