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The Authenticity Gap and the Challenge of Distinguishing AI in Personal and Professional Dialogue
Abstract
The rapid proliferation of Large Language Models (LLMs) has dissolved the linguistic boundaries distinguishing human from machine expression, precipitating a “crisis of authenticity”. This chapter explores the dual challenges arising from this technological inflection point. First, it analyzes the practical “arms race” between generative models and forensic tools, arguing that reliable detection is increasingly infeasible due to the statistical convergence of human and machine text. Second, it examines the ethical and psychological fallout: in personal relationships, the inability to verify authorship erodes trust, while in professional spheres, it threatens institutional integrity. The chapter concludes by proposing a framework for “digital authenticity” that moves beyond futile detection efforts toward cryptographic provenance, digital literacy, and new norms of human-AI collaboration.
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