Crafting a Situated Feminist Praxis for Data Regulation in the Age of Artificial Intelligence
Bibliographic record
Abstract
Background: This analysis critically examines the reliance on universalist truths within big data and artificial intelligence (AI) systems. Drawing on Donna Haraway’s critique of objectivity, it challenges the notion that these technologies are neutral, emphasizing their embedded systemic biases. Analysis: Through a reflection on evolving digital policy frameworks, including the Artificial Intelligence and Data Act (AIDA) and the General Data Protection Regulation (GDPR), this analysis highlights how the prevailing focus on individual rights and self-regulation fails to address the systemic marginalization inherent in AI development. By neglecting structural inequalities, these policies fall short of fostering equity and justice in AI systems. Conclusions and implications: The study concludes with a framework to establish a situated feminist praxis in AI policy. This twofold approach advocates for 1) recontextualizing AI systems to account for systemic biases and 2) ensuring meaningful participation from diverse marginalized communities in policymaking processes.
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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 | 0.038 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.110 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".