Epistemic Subordination: Generative AI and the Infrastructure of Knowledge

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Gilad Abiri, Emanuel V. Towfigh
• Venue: arXiv: cs.CY
• Published: 2026-08-19
• Collected: 2026-08-20
• URL: http://arxiv.org/abs/2608.18758v1
• PDF: https://arxiv.org/pdf/2608.18758v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The paper discusses how Generative AI encodes the dominant culture's way of knowing into its architecture, creating an epistemic condition known as 'epistemic subordination'. This affects minority epistemologies and cuts across three legal domains: anti-discrimination law, cultural and linguistic rights, and democratic viewpoint pluralism. The authors argue that the remedy for this issue should be at the level of model training rather than downstream decisions.

What the Document Says

• Generative AI encodes the dominant culture's way of knowing into its architecture, creating an epistemic condition known as 'epistemic subordination'.
• Minority epistemologies are structurally subordinated in the output of Generative AI, despite being present in the training data.
• The result is not a collection of discrete biases that can be audited and corrected. It is an epistemic condition embedded in the architecture from which all outputs emerge.

Key Concepts Mentioned

• Generative AI
• epistemic subordination
• minority epistemologies

Claims Made by the Document

• Generative AI creates an epistemic condition.
• Minority epistemologies are structurally subordinated in the output of Generative AI.

Evidence Used

• The training process compresses the full breadth of human expression into a single probabilistic model whose statistical baseline reflects the languages, assumptions, and cultural frameworks of the dominant culture.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Generative AI does not merely produce biased outputs. It encodes the majority's way of knowing as the default infrastructure of knowledge itself. We call this epistemic subordination. The training process compresses the full breadth of human expression into a single probabilistic model whose statistical baseline reflects the languages, assumptions, and cultural frameworks of the dominant culture. Minority epistemologies are not excluded but absorbed: present in the training data, yet structurally subordinated in the output. The result is not a collection of discrete biases that can be audited and corrected. It is an epistemic condition embedded in the architecture from which all outputs emerge. This unified harm cuts across three legal domains -- anti-discrimination law, cultural and linguistic rights, and democratic viewpoint pluralism -- and each fails to address it for the same structural reason: existing law regulates downstream, at the level of decisions and applications. The remedy must match the site of harm. If epistemic subordination is produced at the level of model training, then law must learn to govern at that level.