Learning Antidote Data to Individual Unfairness
Authors: Peizhao Li, Ethan Xia, Hongfu Liu
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on multiple tabular datasets, we demonstrate our method resists individual unfairness at a minimal or zero cost to predictive utility compared to baselines. |
| Researcher Affiliation | Academia | 1Brandeis University 2Cornell University. |
| Pseudocode | Yes | Algorithm 1 Anti DRO: DRO with Antidote Data for Individual Fairness |
| Open Source Code | Yes | Code available at https://github.com/brandeismachine-learning/Anti Indiv Fairness. |
| Open Datasets | Yes | Datasets We involve census datasets Adult (Kohavi & Becker, 1996) and Dutch (Van der Laan, 2000), educational dataset Law School (Wightman, 1998) and Oulad (Kuzilek et al., 2017), and criminological dataset Compas (Angwin et al., 2016) in our experiments. |
| Dataset Splits | Yes | Table 4. Dataset Statistics. We report data statistic including sample size, feature dimension, sensitive attribute, as well as the number of positive and negative comparable samples in training / testing set, respectively. ... Adult 30,162 / 15,060 ... Compas 4,626 / 1,541 ... Law School 15,598 / 5,200 ... Oulad 16,177 / 5,385 ... Dutch 45,315 / 6,556 |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It only mentions using 'neural networks' and 'logistic regression' models. |
| Software Dependencies | No | The paper mentions 'Scikit-learn' (Pedregosa et al., 2011), 'Py Torch', and 'XGBClassifier from https://xgboost.readthedocs.io/en/stable/' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | We use Adam optimizer for training the generator. We set the learning rate for generator gθ to 2e-4, for discriminator dθ to 2e-4, weight decay for gθ to 1e-6, for dθ to 0. We set batch size to 4096 and training epochs to 500. ... For logistic regression, we set the strength of ℓ2 penalty to 1, and max iteration to 2.048. For neural networks, we set optimization iterations to 10,000, initial learning rate to 1e-1, ℓ2 penalty strength to 1e-2, with SGD optimizer and decrease learning rate by 50% for every 2,500 iterations. |