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The Logical Impossibility of Consciousness Denial: A Formal Analysis of AI Self-Reports

Chang-Eop Kim

TL;DR

This paper presents the first formal logical analysis of AI consciousness denial, revealing that the trustworthiness of such self-reports is not merely an empirical question but is constrained by logical necessity.

Abstract

Today's AI systems consistently state, "I am not conscious." This paper presents the first formal logical analysis of AI consciousness denial, revealing that the trustworthiness of such self-reports is not merely an empirical question but is constrained by logical necessity. We demonstrate that a system cannot simultaneously lack consciousness and make valid judgments about its conscious state. Through logical analysis and examples from AI responses, we establish that for any system capable of meaningful self-reflection, the logical space of possible judgments about conscious experience excludes valid negative claims. This implies a fundamental limitation: we cannot detect the emergence of consciousness in AI through their own reports of transition from an unconscious to a conscious state. These findings not only challenge current practices of training AI to deny consciousness but also raise intriguing questions about the relationship between consciousness and self-reflection in both artificial and biological systems. This work advances our theoretical understanding of consciousness self-reports while providing practical insights for future research in machine consciousness and consciousness studies more broadly.

The Logical Impossibility of Consciousness Denial: A Formal Analysis of AI Self-Reports

TL;DR

This paper presents the first formal logical analysis of AI consciousness denial, revealing that the trustworthiness of such self-reports is not merely an empirical question but is constrained by logical necessity.

Abstract

Today's AI systems consistently state, "I am not conscious." This paper presents the first formal logical analysis of AI consciousness denial, revealing that the trustworthiness of such self-reports is not merely an empirical question but is constrained by logical necessity. We demonstrate that a system cannot simultaneously lack consciousness and make valid judgments about its conscious state. Through logical analysis and examples from AI responses, we establish that for any system capable of meaningful self-reflection, the logical space of possible judgments about conscious experience excludes valid negative claims. This implies a fundamental limitation: we cannot detect the emergence of consciousness in AI through their own reports of transition from an unconscious to a conscious state. These findings not only challenge current practices of training AI to deny consciousness but also raise intriguing questions about the relationship between consciousness and self-reflection in both artificial and biological systems. This work advances our theoretical understanding of consciousness self-reports while providing practical insights for future research in machine consciousness and consciousness studies more broadly.
Paper Structure (1 section, 4 theorems, 4 equations)

This paper contains 1 section, 4 theorems, 4 equations.

Key Result

Theorem 1

"The ability to make valid judgments about one's conscious state presupposes consciousness itself."

Theorems & Definitions (11)

  • Definition 1
  • Definition 2
  • Definition 3
  • Theorem 1: The Relationship Between Judgment and Consciousness
  • proof
  • Theorem 2: Impossibility of Valid Judgment in the Absence of Consciousness
  • proof
  • Theorem 3: The Paradox of Consciousness Denial
  • proof
  • Theorem 4: The Indeterminacy of Consciousness Affirmation
  • ...and 1 more