Algorithmic Discrimination in Europe: Challenges and Opportunities for Gender Equality and Non-Discrimination Law
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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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".