Bibliographic record
Abstract
Algorithms are increasingly used across all layers of society, including in high-stake decision systems, impacting individuals, cohorts and society as a whole. This omnipresence of algorithms raises important ethical and social concerns, including in particular privacy and fairness. In this thesis, we study these two technical subjects under the lens of their practical usage and strong societal requirements. In a first contribution we investigate the conflict between privacy and transparency when publishing legal proceedings. In a second contribution, we propose a framework to organize privacy challenges with a focus on attacking privacy-preserving data publishing mechanisms to better define their behavior in practice. As a third contribution, we focus on the limits of technical fairness definitions and leverage a simulation grounded in reality as a way to observe the long-term impact of fairness on a whole system. Overall, the main objective of this thesis is to highlight fundamental issues raised by the technicization of social challenges as well as to propose technical tools and analyses aimed towards bringing these sociotechnical challenges back to their social context.
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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 | 0.039 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".