Epistemic Injustice in and through AI
Bibliographic record
Abstract
AI is rapidly transforming knowledge production and practices across a range of domains, yet AI technologies often embed and perpetuate epistemic injustices—privileging dominant perspectives while marginalising others. Despite growing awareness of AI biases, many frameworks used in HCI and AI ethics fail to fully account for how AI models reproduce historical and systemic exclusions. We propose to critically examine epistemic injustice in AI across six domains; generative AI, creative practice, healthcare, work, education and automated decision-making. We explore how AI systems respond to diverse sociocultural, linguistic, and epistemological inputs, revealing biases in representation, accessibility, and credibility. Through reflection and collaborative mapping, we aim to identify research priorities and intervention strategies at individual, community, and broader society levels. By fostering rich dialogue and nuanced evidencing, we seek to advance research on epistemic justice in AI and create pathways for more inclusive and equitable futures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.126 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".