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

Joel Sadiki Charo; Kennedy Ondimu

doi:doi:10.11648/j.mlr.20261102.15

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

Joel Sadiki Charo^*

, Kennedy Ondimu

Published in Machine Learning Research (Volume 11, Issue 2)

Received: 19 September 2026 Accepted: 4 October 2026 Published: 29 October 2026

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Abstract

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.

Published in	Machine Learning Research (Volume 11, Issue 2)
DOI	10.11648/j.mlr.20261102.15
Page(s)	128-136
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Epistemic Injustice, Algorithmic Systems, Artificial Intelligence, Testimonial Injustice, Hermeneutical Injustice, AI Ethics

References

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[2]	Burrell, J. (2016). How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512
[3]	Dotson, K. (2011). Tracking epistemic violence, tracking practices of silencing. Hypatia, 26(2), 236-257.
[4]	Floridi, L. (2013). The ethics of information. Oxford University Press.
[5]	Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.
[6]	Furst, M. (2024, January 1). Closing the Conceptual Gap in Epistemic Injustice. The Philosophical Quarterly.
[7]	Hull, G. (2023, June 15). Dirty Data Labeled Dirt Cheap: Epistemic Injustice in Machine Learning Systems.
[8]	Kalluri, P. (2020). Don't ask if AI is good or fair, ask how it shifts power. Nature, 583, 169. https://doi.org/10.1038/d41586-020-02003-2
[9]	Kay, J. K. (2024). Epistemic Injustice in Generative AI. Proceedings of the AAAI ACM Conference on AI, Ethics, and Society, 7(1), 684-697.
[10]	Medina, J. (2013). The epistemology of resistance: Gender and racial oppression, epistemic injustice, and resistant imaginations. Oxford University Press.
[11]	Miedema, E. (2025). Towards trustworthy artificial intelligence for decision-making: A lifecycle perspective on knowledge- and data-driven artificial intelligence systems.
[12]	Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679679
[13]	Mollema, W. J. (2025). A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure.
[14]	Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
[15]	Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency, 59-68. https://doi.org/10.1145/3287560.3287598
[16]	Xu, Z. (2023). A Reusable Model-agnostic Framework for Faithfully Explainable Recommendation and System Scrutability.

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Charo, J. S., Ondimu, K. (2026). Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production. Machine Learning Research, 11(2), 128-136. https://doi.org/10.11648/j.mlr.20261102.15

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Charo, J. S.; Ondimu, K. Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production. Mach. Learn. Res. 2026, 11(2), 128-136. doi: 10.11648/j.mlr.20261102.15

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Charo JS, Ondimu K. Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production. Mach Learn Res. 2026;11(2):128-136. doi: 10.11648/j.mlr.20261102.15

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@article{10.11648/j.mlr.20261102.15,
  author = {Joel Sadiki Charo and Kennedy Ondimu},
  title = {Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production},
  journal = {Machine Learning Research},
  volume = {11},
  number = {2},
  pages = {128-136},
  doi = {10.11648/j.mlr.20261102.15},
  url = {https://doi.org/10.11648/j.mlr.20261102.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20261102.15},
  abstract = {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.},
 year = {2026}
}

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TY  - JOUR
T1  - Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production
AU  - Joel Sadiki Charo
AU  - Kennedy Ondimu
Y1  - 2026/10/29
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AB  - 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.
VL  - 11
IS  - 2
ER  -

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Author Information

Joel Sadiki Charo

Institute of Computing and Informatics, Technical University of Mombasa, Mombasa, Kenya

Contact Email

http://orcid.org/0009-0009-0398-2242
Kennedy Ondimu

Institute of Computing and Informatics, Technical University of Mombasa, Mombasa, Kenya

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APA Style

Charo, J. S., Ondimu, K. (2026). Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production. Machine Learning Research, 11(2), 128-136. https://doi.org/10.11648/j.mlr.20261102.15

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ACS Style

Charo, J. S.; Ondimu, K. Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production. Mach. Learn. Res. 2026, 11(2), 128-136. doi: 10.11648/j.mlr.20261102.15

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AMA Style

Charo JS, Ondimu K. Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production. Mach Learn Res. 2026;11(2):128-136. doi: 10.11648/j.mlr.20261102.15

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@article{10.11648/j.mlr.20261102.15,
  author = {Joel Sadiki Charo and Kennedy Ondimu},
  title = {Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production},
  journal = {Machine Learning Research},
  volume = {11},
  number = {2},
  pages = {128-136},
  doi = {10.11648/j.mlr.20261102.15},
  url = {https://doi.org/10.11648/j.mlr.20261102.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20261102.15},
  abstract = {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.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Epistemic Injustice in Algorithmic Systems: A Frickerian Analysis of AI-Driven Knowledge Production
AU  - Joel Sadiki Charo
AU  - Kennedy Ondimu
Y1  - 2026/10/29
PY  - 2026
N1  - https://doi.org/10.11648/j.mlr.20261102.15
DO  - 10.11648/j.mlr.20261102.15
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 128
EP  - 136
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20261102.15
AB  - 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.
VL  - 11
IS  - 2
ER  -

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