Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Protecting the Protected Group: Circumventing Harmful Fairness
Authors: Omer Ben-Porat, Fedor Sandomirskiy, Moshe Tennenholtz5176-5184
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we uncover another mechanism underlying harmful fairness even in static settings: Imposing a fairness constraint can make the disadvantaged group worse off if the fairness constraint and the utilities of the population mismatch. [...] Finally, we suggest additional ways to deal with the mismatch if the underlying utilities can be approximated from data. [...] In this section, we provide evidence for the applicability of WE-fairness by developing tools for computing bank-optimal WE classifiers. Our goal is to show how the bank can use the assumed utility proposed by the regulator to compute approximately optimal classifiers. [...] Proposition 8. Fix a small δ > 0 and assume that the bank has access to a sample of (X, A, Y, α , v) and to estimators u and ˆr such that E [|u v|] ηu and E [|ˆr r|] ηr for small enough ηu and ηr. Then, a (ε, ε) bank-optimal v-WE classifier with 6 1 P(A = 0) + 1 P(A = 1) max{ηu, ηr} can be computed with probability 1 δ on a sample of size O 1 max{ηu, ηr} δ + log log 1 max{ηu, ηr} |
| Researcher Affiliation | Academia | Omer Ben-Porat1, Fedor Sandomirskiy2,3, Moshe Tennenholtz2 1Tel-Aviv University 2Technion Israel Institute of Technology 3Higher School of Economics, St. Petersburg, Russia |
| Pseudocode | No | The paper does not contain any pseudocode blocks or clearly labeled algorithm sections. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions working with "historical data" and presents a numerical "Example 3" with hypothetical probabilities and revenue/loss values. However, it does not provide access information (link, DOI, citation to a public source) for any specific dataset used in an empirical study. |
| Dataset Splits | No | The paper is theoretical and uses a numerical example (Example 3) for illustration. It does not describe experiments that would involve training, validation, or test dataset splits. Therefore, no information on these splits is provided. |
| Hardware Specification | No | The paper does not specify any hardware used for the computations or analysis described. It focuses on theoretical concepts and mathematical models. |
| Software Dependencies | No | The paper does not list any specific software or library dependencies with version numbers. It primarily discusses theoretical models and mathematical properties. |
| Experiment Setup | No | The paper does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings, as it is primarily a theoretical paper with a numerical example. |