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The aim is to explore how generative AI causes epistemic injustice through specific mechanisms, impacting knowledge quality.

March 10, 2026Open Access

Epistemic Injustice in Generative AI: Probabilistic Generation, Trust Erosion, and the Structural Conditions of Algorithmic Knowledge Harm

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The aim is to explore how generative AI causes epistemic injustice through specific mechanisms, impacting knowledge quality.
Analysis of generative AI as epistemic intermediaries
Examination of Fricker's framework and its application to LLMs
Introduction of Algorithmic Gettier Cases as a new analytical concept
Development of a governance framework focusing on transparency and inclusivity.
Identified three mechanisms contributing to epistemic injustice: automation-induced testimonial injustice, interpretive erasure, and epistemic debt accumulation.
Proposed the concept of Algorithmic Gettier Cases to highlight structural failures in AI outputs.
Suggested a governance framework centered on testimonial transparency and structural accountability.

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Demonstrates the impact of generative AI on epistemic injustice, suggesting important governance mechanisms.

Shenghui Bao (2026) studied this question.

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