Consciousness in Artificial Intelligence? A Framework for Classifying Objections and Constraints

2025-11-20

AI Summary

Digital AI consciousness faces diverse philosophical objections, yet existing debates suffer from conceptual conflation and lack a systematic framework to distinguish their theoretical scope (e.g., computational, algorithmic, or implementational levels à la Marr) and logical force (from challenging functionalism to asserting strict impossibility). Method: We develop the first two-dimensional taxonomy integrating Marr’s tripartite hierarchy with gradations of argumentative strength. Through rigorous conceptual analysis and comprehensive literature review, we classify and deconstruct 14 representative anti-consciousness arguments. Contribution/Results: The taxonomy resolves persistent ambiguities by clarifying logical dependencies, demarcating stance boundaries, and exposing implicit assumptions. It enhances precision and interoperability across consciousness science, AI ethics, and philosophy of mind, yielding a reusable, structured analytical tool for evaluating claims about artificial consciousness.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Philosophy and Ethics of AI: Philosophical Foundations of AI
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

We develop a taxonomical framework for classifying challenges to the possibility of consciousness in digital artificial intelligence systems. This framework allows us to identify the level of granularity at which a given challenge is intended (the levels we propose correspond to Marr's levels) and to disambiguate its degree of force: is it a challenge to computational functionalism that leaves the possibility of digital consciousness open (degree 1), a practical challenge to digital consciousness that suggests improbability without claiming impossibility (degree 2), or an argument claiming that digital consciousness is strictly impossible (degree 3)? We apply this framework to 14 prominent examples from the scientific and philosophical literature. Our aim is not to take a side in the debate, but to provide structure and a tool for disambiguating between challenges to computational functionalism and challenges to digital consciousness, as well as between different ways of parsing such challenges.

Problem

• Classifying challenges to consciousness in AI systems
• Disambiguating degrees of force against computational functionalism
• Providing framework to analyze scientific and philosophical objections

Innovation

• Developed taxonomical framework for classifying AI consciousness challenges
• Identified granularity levels based on Marr's computational theory
• Applied framework to 14 scientific and philosophical literature examples