Generalizing Group Fairness in Machine Learning via Utilities
Authors: Jack Blandin, Ian A. Kash
JAIR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we provide an experimental analysis on an environment where classification fairness metrics fail to appropriately measure fairness due to Assumption 1. In order to ensure that our analysis is consistent with other group fairness works, we leverage the fairness-comparison benchmark of Friedler et al. for data preprocessing, algorithm implementation, and fairness measurement calculations (Friedler et al., 2019). |
| Researcher Affiliation | Academia | Jack Blandin EMAIL Ian A. Kash EMAIL University of Illinois at Chicago, Department of Computer Science, Chicago, IL 60607 USA |
| Pseudocode | No | The paper describes its framework and methodology using definitions, equations, and prose, but does not include any explicitly labeled pseudocode or algorithm blocks with structured steps. |
| Open Source Code | Yes | The full code repository to reproduce the results in this paper is available at https://github.com/jackblandin/group-fairness-in-machine-learning-via-utilities. |
| Open Datasets | Yes | Dataset We consider the loan application scenario described by the German Credit Dataset (Dua & Graff, 2017), which consists of 1,000 loan application records. |
| Dataset Splits | Yes | Results We execute and measure each algorithm using 10-fold cross-validation. For each performance measurement, we report the average value as well as the 10th and 90th percentiles. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions evaluating |
| Experiment Setup | No | The paper describes the algorithms evaluated (Decision Tree, Support Vector Machine, Feldman Decision Tree, Feldman SVM, Feldman Logistic Regression, Zafar Fair) and their general approaches, as well as the parameters for the utility fairness framework (W, C, τ, ρ) and the use of 10-fold cross-validation. However, it does not provide specific hyperparameters (e.g., learning rate, batch size, number of epochs) for the training of these machine learning models. |