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Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Identifying indicators of consciousness in AI systems.

Patrick Butlin¹, Robert Long², Tim Bayne³

¹Global Priorities Institute, University of Oxford, Oxford, UK; Eleos AI Research, Berkeley, CA, USA.

Trends in Cognitive Sciences

|November 11, 2025

Summary

Assessing artificial intelligence (AI) consciousness requires rigorous methods. This study proposes a novel approach using neuroscientific theories to develop empirical indicators for evaluating AI consciousness, particularly computational functionalist theories.

Keywords:

artificial intelligence computational functionalism consciousness tests for consciousness theories of consciousness

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Area of Science:

Neuroscience
Artificial Intelligence
Philosophy of Mind

Background:

Rapid advancements in artificial intelligence (AI) necessitate a re-evaluation of consciousness in non-biological systems.
Significant gaps exist in current consciousness science, hindering the development of reliable methods for AI consciousness assessment.

Purpose of the Study:

To present a novel methodology for assessing consciousness in artificial intelligence systems.
To bridge the gap between consciousness science and AI development by leveraging neuroscientific theories.

Main Methods:

The proposed method involves deriving testable indicators from existing and future neuroscientific theories of consciousness.
Focuses on theories, such as computational functionalism, with direct implications for artificial intelligence.

Main Results:

The framework allows for the generation of empirical evidence to inform beliefs about AI consciousness.
Identifies specific implications of influential consciousness theories that can be investigated in AI.

Conclusions:

The presented method offers a rigorous pathway to empirically investigate AI consciousness.
Progress in understanding AI consciousness is achievable by grounding assessments in established or developing neuroscientific frameworks.