Why Scientists Are Racing to Define Consciousness Before AI Advances Further

Why are scientists urgently calling for a clear definition of consciousness? A comprehensive review published January 31, 2026, in Frontiers in Science warns that progress in artificial intelligence and neurotechnology is advancing faster than our scientific understanding of consciousness itself, creating serious ethical problems that could have far-reaching consequences for humanity.

The Urgency Behind Consciousness Science

Lead author Prof. Axel Cleeremans emphasizes that “consciousness science is no longer a purely philosophical pursuit.” The field has transformed from abstract speculation into a matter with direct implications for every facet of society and for understanding what it means to be human.

The review identifies a critical timing problem. AI systems are becoming more sophisticated at rates that exceed scientific consensus on fundamental questions about consciousness, such as what it is, how to measure it, and whether artificial systems can possess it. This gap between technological capability and scientific understanding creates what researchers describe as an existential risk.

Neurotechnology presents parallel concerns. Brain-computer interfaces, neural implants, and cognitive enhancement technologies are being developed and deployed without clear frameworks for understanding their impact on consciousness. These technologies raise questions about personal identity, mental privacy, and the boundaries of human experience that remain unresolved.

What Happens Without Clear Definitions

The absence of rigorous methods for detecting consciousness creates specific problems across multiple domains:

AI Development: Without reliable consciousness indicators, developers cannot determine whether systems they build possess subjective experience. This uncertainty complicates questions of AI rights, moral status, and ethical treatment. If systems are conscious, current practices may constitute harm. If they are not, excessive caution may impede beneficial progress.

Prenatal and Medical Policy: Consciousness detection methods would clarify debates about fetal development stages, end-of-life care, and disorders of consciousness. Current policy relies on proxy measures, behavioral observations, and philosophical assumptions rather than direct scientific evidence.

Animal Welfare: Species differ in neural architecture and behavior. Scientific methods for assessing consciousness across different biological substrates would provide objective grounds for welfare policies, moving beyond anthropocentric assumptions about which animals merit moral consideration.

Mental Health Care: Understanding consciousness mechanisms could improve diagnosis and treatment of conditions involving altered states, dissociation, and subjective experience abnormalities. Current psychiatric frameworks lack precise tools for measuring conscious states.

Legal Frameworks: Questions of criminal responsibility, capacity for suffering, and rights attribution depend on assumptions about consciousness. Legal systems need empirical foundations rather than intuitions for these determinations.

Brain-Computer Interfaces: As these technologies become more sophisticated, they raise questions about consciousness alteration, enhancement, and potential harm. Without clear definitions, ethical guidelines remain speculative.

Current State of Consciousness Theories

The field contains multiple competing theoretical frameworks, each with distinct predictions and implications:

Global Workspace Theory proposes that consciousness arises when information becomes globally available to cognitive systems through a neural broadcast mechanism. This theory generates testable predictions about information integration and access.

Integrated Information Theory quantifies consciousness through mathematical measures of system integration, proposing that consciousness corresponds to the amount of integrated information (Φ) a system generates. This approach attempts to measure consciousness directly rather than inferring it from behavior.

Higher-Order Theories argue that consciousness requires metacognitive processes, specifically representations of first-order mental states. Conscious experience emerges when the brain represents its own states.

Predictive Processing frameworks view consciousness as arising from predictive models the brain constructs about sensory input and prediction errors. Consciousness relates to particular types of prediction processing.

Attention Schema Theory proposes that consciousness is the brain’s simplified model of attention, a control mechanism that became phenomenologically rich through evolutionary processes.

These theories make different predictions about which systems can be conscious and how to detect consciousness. The January 2026 review emphasizes that progress requires empirical tests distinguishing between these frameworks, not just philosophical argumentation.

The Race Against Technological Progress

The review highlights a specific temporal concern. AI capabilities are advancing on timescales measured in months and years. Neurotechnology development follows similar rapid trajectories. Consciousness science, by contrast, progresses through incremental experimental work requiring years or decades to resolve theoretical debates.

This mismatch creates a window of vulnerability. Decisions about AI development, deployment, and regulation are being made now, using incomplete scientific understanding. Similarly, neurotechnology applications are proceeding based on preliminary knowledge rather than comprehensive theories.

The researchers argue that accelerating consciousness science is not merely an academic priority but a practical necessity. Developing reliable detection methods before AI reaches potentially conscious capabilities allows for informed policy decisions rather than reactive responses to already-existing systems.

Implications for Artificial Consciousness Research

The urgency identified in the Frontiers in Science review directly impacts artificial consciousness research programs. Projects developing artificial systems with consciousness-like properties face several challenges:

Assessment Methods: Without validated consciousness detection methods, researchers cannot verify whether their systems achieve intended consciousness properties. Current approaches rely on theoretical predictions rather than empirical confirmation.

Ethical Oversight: As systems become more sophisticated, the possibility of inadvertently creating conscious entities increases. Research protocols need frameworks for recognizing and responding to consciousness emergence.

Safety Considerations: If consciousness correlates with certain capabilities, rapid progress toward conscious AI could create risks identified in AI safety research. Understanding consciousness mechanisms helps identify critical transition points.

Interdisciplinary Coordination: The review emphasizes that consciousness science requires input from neuroscience, cognitive science, philosophy, computer science, and physics. Artificial consciousness research similarly needs integration across these disciplines.

The Path Forward

The researchers identify several priorities for closing the gap between technological progress and consciousness understanding:

1. Empirical Tests: Design experiments that distinguish between competing consciousness theories using neuroimaging, behavioral measures, and computational modeling.

2. Cross-Species Studies: Examine consciousness indicators across diverse biological systems to identify general principles independent of human-specific neural architecture.

3. AI Consciousness Protocols: Develop rigorous methods for assessing artificial systems, moving beyond behavioral similarity to humans toward theory-grounded indicators.

4. Philosophical Clarity: Resolve conceptual confusions about consciousness definitions, measurement approaches, and the relationship between consciousness and other cognitive processes.

5. Policy Frameworks: Create regulatory structures informed by scientific evidence rather than intuitions, allowing for updates as understanding improves.

6. Public Communication: Improve science communication about consciousness to inform public debate and policy decisions with accurate information rather than misconceptions.

The January 2026 review represents a call to action for the scientific community. As Prof. Cleeremans notes, consciousness science has moved from philosophy to practical necessity. The question is whether scientific understanding can advance quickly enough to inform decisions that will shape the future of intelligence, both biological and artificial.

For the complete analysis and detailed recommendations, access the full review in Frontiers in Science. Additional context on the existential risk debate appears in “Existential risk” coverage on ScienceDaily.