Bibliographic record
Abstract
Disability advocacy organizations need access to disability data to advocate for human rights, disability justice, and sustainable development goals. Health informatics and Artificial Intelligence that has been developed through an equity-focused process provide important tools that can help address these needs. AI is prone to bias and might exacerbate discrimination and data ableism. A critical disability lens and community collaboration can help to address these biases, and multidisciplinary collaborations with grassroots and community-based organizations are crucial for advancing disability data justice. We have been engaged in a practice-led approach to building a disability justice-focused AI search engine. In the first section of this paper, we report on our co-design process, which included community consultations about disability data justice with local, provincial, national, transnational, and supernational disability organizations and advocates. In the second section, we demonstrate how we applied a transnational disability studies framework during our process of training a search engine AI to function from a disability justice perspective. We demonstrate the semantic and conceptual differences between a transnational approach and a disability rights approach, as a concrete example of how AI bias emerges. We argue that our participatory approach allows us to experiment with data repositories and search engines that confront AI bias and data ableism and reflect community needs.
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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 | 0.057 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.021 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".