Conformism, Ignorance & Injustice: AI as a Tool of Epistemic Oppression
Open accessAbstract
From music recommendation to assessment of asylum applications, machine-learning algorithms play a fundamental role in our lives. Naturally, the rise of AI implementation strategies has brought to public attention the ethical risks involved. However, the dominant anti-discrimination discourse, too often preoccupied with identifying particular instances of harmful AIs, has yet to bring clearly into focus the more structural roots of AI-based injustice. This paper addresses the problem of AI-based injustice from a distinctively epistemic angle. More precisely, I argue that the injustice generated by the implementation of AI machines in our societies is, in some paradigmatic cases, also a form of epistemic injustice. With a particular focus on AIs employed as gatekeepers of our epistemic resources, this paper shows how their epistemically conformist behaviour is responsible for the marginalisation and the ostracism of minoritarian perspectives. Because it clarifies key structural flaws and weaknesses of current AI design, this paper helps make headway in critical discussion of current AI technologies. And because it forges new theoretical tools to understand forms of epistemic oppression, this paper also contributes to the advancement of feminist theorisation.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In Miragoli's work, which is tailored to the context of AI, two more general forms of epistemic injustice stand out.
“Finally, in the work of Martin Miragoli [33], which is actually already tailored to the context of AI, two more general forms of epistemic injustice stand out.”
From A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure (2025) · cites this paper as [33]
AI systems can negatively affect the capacity of knowledge seeking.
“Giving testimony concerns one's role as knowledge giver, but, as we learned from Miragoli, AI systems can also negatively affect the capacity of knowledge seeking.”
From A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure (2025) · cites this paper as Miragoli [33]
Search algorithms frame the outcomes of a query based on principles or statistical design that doesn't necessarily align with the intentions of the inquirer, highlighting some sources of information while hiding others.
“The example Miragoli gives is that of search algorithms framing the outcomes of a query based on a principles or statistical design that doesn't necessarily align with the intentions of the inquirer, highlighting some sources of information, while hiding others.”
From A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure (2025) · cites this paper as Miragoli [33]
Algorithmic systems can frustrate access to knowledge and emborder avenues for inquiry via mediating the dissemination of knowledge towards specific knowers.
“Algorithmic systems can frustrate access to knowledge and emborder avenues for inquiry via mediating the dissemination of knowledge towards specific knowers.”
From A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure (2025) · cites this paper as Miragoli [33]
Zetetic injustice is especially salient in the context of algorithmic systems.
“According to Miragoli [33], this is also especially salient in the context of algorithmic systems.”
From A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure (2025) · cites this paper as Miragoli [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 19 · Cited by 31
Research works printed but not linked: 16
- Adam A. (1998). Artificial Knowing: Gender and the Thinking Machine. London, UK: Routledge.
- Bagenstos S.R. (2006). 'The Structural Turn and the Limits of Antidiscrimination Law.' California Law Review 94(1), 1-47.
- Browne S. (2015). Dark Matters. Durham, UK: Duke University Press.
- Du Bois W.E.B. (1989). The Souls of Black Folk, Orig. ed. 1903. New York: Penguin.
- Gandy O.H. (1998). Communication and Race: A Structural Perspective. New York, US: Edward Arnold and Oxford University Press.
- Huang L.T.-L., Chen H.-Y., Lin Y.-T., Huang T.-R. and Hung T.-W. (2022). 'Ameliorating Algorithmic Bias, or Why Explainable AI Needs Feminist Philosophy.' Feminist Philosophy Quarterly 8(3/4), 1-31.
- Kimmerer R.W. (2013). Braiding Sweetgrass. Milkweed Editions. New York: Penguin.
- Krieger L.H. (1995). 'The Content of our Categories: A Cognitive Bias Approach to Discrimination and Equal Employment Opportunity.' Stanford Law Review 47(6), 1161-248.
- Martín A. (2021). 'What Is White Ignorance.' The Philosophical Quarterly 71(4), 864-85.
- Mills C.W. (1997). The Racial Contract. Ithaca, NY: Cornell University Press.
- Noble S. (2018). Algorithms of Oppression. New York, US: NYU Press.
- Rafanelli L.M. (2022). 'Justice, Injustice, and Artificial Intelligence: Lessons from Political Theory and Philosophy.' Big Data & Society, 9(1), 1-5.
- Simion M. and Kelp C. (2023). 'Trustworthy Artificial Intelligence.' Asian Journal of Philosophy, 2(1), 1- 12. https://doi.org/10.1007/s44204-023-00063-5. · DOI
- Spewak D. (2023). 'Perlocutionary Silencing: A Linguistic Harm That Prevents Discursive Influence.' Hypatia, 38(1), 86-104. doi:10.1017/hyp.2023. · DOI
- Spivak G.C. (1999). A Critique of Postcolonial Reason: Toward a History of the Vanishing Present. Cambridge, MA: Harvard University Press.
- Tanesini A. (2016). "Calm Down, Dear": Intellectual Arrogance, Silencing and Ignorance. Aristotelian Society Supplementary, 90(1), 71-92.
- Authors
- Miragoli, M.
- Affiliations
- University of Glasgow (GB)
- Corresponding author
- Miragoli, Martin
- ORCID
- Miragoli, Martin
- Venue
- Episteme
- Publisher
- Cambridge University Press (CUP)
- Published
- 2024-04-08
- Volume
- 22
- Issue
- 2
- Pages
- 522-540
- ISSN
- 1742-3600 (ISSN-L)
- 1750-0117
- OpenAlex
- W4394576411
- Cited by
- 31
- References
- 19
- Citations by year
- 2026: 21 · 2025: 10
- FWCI
- 22.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Episteme (publishedVersion, cc-by)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Funders
- University of Cambridge
- Topics
- Social Sciences › Sociology and Political Science › Feminist Epistemology and Gender Studies
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Arts and Humanities › Philosophy › Epistemology, Ethics, and Metaphysics
- Keywords
- epistemic injustice, epistemic oppression, algorithmic discrimination, gatekeeping, feminist theory, structural injustice, algorithmic bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
