Algorithmic Discrimination in Europe: Challenges and Opportunities for Gender Equality and Non-Discrimination Law
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid development and increasing use of artificial intelligence and algorithmic applications have raised many concerns relating to algorithms’ propensity to discriminate. Algorithmic discrimination can arise from various sources and at various stages of software design and it risks endangering one of the most fundamental rights guaranteed by EU law: the right to gender equality and non-discrimination. This thematic report identifies the main legal challenges arising from algorithmic discrimination at both national and EU level. It assesses whether the current gender equality and non-discrimination legislative framework in place in the EU and at the national level adequately captures algorithmic discrimination. It maps out the gaps and weaknesses that arise from the interaction between the specific types of discrimination produced by algorithmic decision-making systems on the one hand and the particular material and personal scope of existing legislative frameworks on the other. This thematic report also examines which legal solutions, policy measures and good practices the EU and the national member states have adopted to address these gaps and weaknesses. In short, this thematic report investigates how the issue of algorithmic discrimination is framed, addressed and redressed in the EU, with a particular focus on gender equality.<br/><br/>Thematic Report coordinated by the European Network of legal experts in gender equality and non-discrimination
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 it