Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production

Artificial intelligence is playing a great role in the production, classification, retrieval, and communication of knowledge. Tools like large language models, recommender systems, automated decision-making systems, and search engines do more than just process information; they shape whose testimony is heard, which concepts become available for interpreting experience, and which claims are treated as credible or important. This paper argues that Miranda Fricker's theory of epistemic injustice offers a strong framework to ethically identify shortcomings in AI-enabled knowledge production; however, it also suggests that, the distinction made in classical accounts between testimonial and hermeneutical injustices needs to be extended in order to address them in the context of sociotechnical systems. Testimonial injustice can arise when algorithmic models reproduce credibility deficits through biased training data, proxy variables, ranking practices, moderation systems, or automated classification. On the other hand, hermeneutical injustice can emerge when certain communities are poorly represented in the conceptual and linguistic tools that AI systems use to create explanations, categories, and interpretations. The paper further argues that the opaque nature of many AI systems intensifies these harms because affected persons may be unable to understand, contest, or correct the epistemic processes that affect them. Building on Fricker, Floridi, Dotson, Medina, and contemporary work in AI ethics, the paper develops an epistemic justice framework centred on adequacy in represantation, credibility protection, interpretive inclusion, contestability, provenance, and institutional accountability. The proposed framework treats epistemic justice not merely as a desirable output property but as a design and governance requirement for AI systems that participate in consequential knowledge practices.