Bibliographic record
Abstract
Artificial intelligence (AI) presents immense potential and significant challenges concerning algorithmic bias. This paper explores how feminist theory provides a criti-cal lens for understanding and addressing algorithmic bi-as’s root causes and impacts. The historical context of systemic discrimination reveals how power imbalances have shaped data collection and analysis, leading to bi-ased datasets that perpetuate inequalities through AI sys-tems. The "black box" problem further obscures these bi-ases, amplifying discriminatory outcomes in various domains. Feminist interventions, particularly intersec-tional feminism, offer a framework for uncovering how algorithmic bias interacts with multiple forms of oppres-sion. Feminist data science challenges traditional meth-odologies and advocates for transparency, accountabil-ity, and diversity in AI development. Critiques of tech-no-solutionism highlight the need for broader societal change alongside technical fixes. By embracing a feminist approach, we can envision and work toward a future where AI technology is used for social justice, inclusivi-ty, and collective liberation.
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.068 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".