Ensuring Fairness Beyond the Training Data
Authors: Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette Wing, Daniel J. Hsu
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on standard machine learning fairness datasets suggest that, compared to the state-of-the-art fair classifiers, our classifier retains fairness guarantees and test accuracy for a large class of perturbations on the test set. Furthermore, our experiments show that there is an inherent trade-off between fairness robustness and accuracy of such classifiers. |
| Researcher Affiliation | Academia | Debmalya Mandal EMAIL Columbia University Samuel Deng EMAIL Columbia University Suman Jana EMAIL Columbia University Jeannette M. Wing EMAIL Columbia University Daniel Hsu EMAIL Columbia University |
| Pseudocode | Yes | ALGORITHM 1: Meta-Algorithm, ALGORITHM 2: Best Response of the -player, ALGORITHM 3: Approximate Fair Classi er (Apx Fair) |
| Open Source Code | Yes | Our code is available at this Git Hub repo: https://github.com/essdeee/ Ensuring-Fairness-Beyond-the-Training-Data. |
| Open Datasets | Yes | We used the following four datasets for our experiments. Adult. In this dataset [24], each example represents an adult individual... Communities and Crime. In this dataset from the UCI repository [29]... Law School. We used a preprocessed and balanced subset with 1,823 examples and 17 features [33]. COMPAS. We used a 2,000 example sample from the full dataset. For Adult, Communities and Crime, and Law School we used the preprocessed versions found in the accompanying Git Hub repo of [22]4. For COMPAS, we used a sample from the original dataset [1]. |
| Dataset Splits | Yes | In order to evaluate different fair classi ers, we rst split each dataset into ve different random 80%-20% train-test splits. Then, we split each training set further into a 80%-20% train and validation sets. Therefore, there were ve random sets of 64%-16%-20% train-validation-test split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, cloud instances) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions using 'scikit-learn’s logistic regression [27]' but does not provide specific version numbers for scikit-learn or other software dependencies. |
| Experiment Setup | Yes | To nd the correct hyper-parameters (B, , T, and Tm) for our algorithm, we xed T = 10 for EO, and T = 5 for DP, and used grid search for the hyper-parameters B, , and Tm. The tested values were {0.1, 0, 2, . . . , 1} for B, {0, 0.05, . . . , 1} for , and {100, 200, . . . , 2000} for Tm. |