Protecting the Protected Group: Circumventing Harmful Fairness
Authors: Omer Ben-Porat, Fedor Sandomirskiy, Moshe Tennenholtz5176-5184
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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. |