Algorithmic Price Personalization and the Limits of Anti-Discrimination Law
Bibliographic record
Abstract
As much attention is turned to regulating AI systems to minimize the risk of harm, including the one caused by discriminatory biased outputs, a better understanding of how commercial practices may contravene anti-discrimination law is critical. This article investigates the instances in which algorithmic price personalization, (i.e., setting prices based on consumers’ personal information with the objective of getting as close as possible to their maximum willingness to pay (APP)), may violate anti-discrimination law. It analyses cases whereby APP could constitute prima facie discrimination, while acknowledging the difficulty to detect this commercial practice. It discusses why certain commercial practice differentiations, even on prohibited grounds, do not necessarily lead to prima facie discrimination, offering a more nuanced account of the application of anti-discrimination law to APP. However once prima facie discrimination is established, APP will not be easily exempted under a bona fide requirement, given APP’s lack of a legitimate business purpose under the stringent test of anti-discrimination law, consistent with its quasi-constitutional status. This article bridges traditional anti-discrimination law with emerging AI governance regulation. Pointing to identified gaps in anti-discrimination law, it analyses how AI governance regulation could enhance anti-discrimination law and improve compliance.
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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 | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.015 |
| 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".