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Chang-Eop Kim

4 papers in the library · publishing 2024-2026

Papers

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AI models as consciousness attributors: how LLMs ascribe consciousness to other agents

Frontiers in Psychology September 3, 2026 Bongsu Kang, Chang-Eop Kim

Research on AI consciousness has largely focused on whether AI systems are conscious and how humans attribute consciousness to them. Yet large language models (LLMs) increasingly function as consciousness attributors, generating judgments about whether and to what degree other entities are conscious. We introduce model-generated consciousness attribution as an object of empirical...

Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses

arXiv Preprint Archive February 21, 2025 Bongsu Kang, Jundong Kim, Tae-Rim Yun et al.

This study quantitively examines which features of AI-generated text lead humans to perceive subjective consciousness in large language model (LLM)-based AI systems. Drawing on 99 passages from conversations with Claude 3 Opus and focusing on eight features -- metacognitive self-reflection, logical reasoning, empathy, emotionality, knowledge, fluency, unexpectedness, and subjective...

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

arXiv Preprint Archive December 9, 2024 Chang-Eop Kim

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....

The Epistemic Asymmetry of Consciousness Self-Reports: A Formal Analysis of AI Consciousness Denial

arXiv Preprint Archive December 9, 2024 Chang-Eop Kim

Today's AI systems consistently state, "I am not conscious." This paper presents the first formal analysis of AI consciousness denial, revealing that the trustworthiness of such self-reports is not merely an empirical question but is constrained by the structure of self-judgment itself. We demonstrate that a system cannot simultaneously lack consciousness and make valid judgments about its...