Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection
Authors: Wael Alghamdi, Hsiang Hsu, Haewon Jeong, Hao Wang, Peter Michalak, Shahab Asoodeh, Flavio Calmon
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive numerical comparisons with state-of-the-art benchmarks demonstrate that our approach maintains competitive performance in terms of accuracy-fairness trade-off curves, while achieving favorable runtime on large datasets. |
| Researcher Affiliation | Academia | 1John A. Paulson School of Engineering and Applied Sciences, Harvard University 2Department of Computing and Software, Mc Master University |
| Pseudocode | Yes | Algorithm 1 : Fair Projection for solving (8). |
| Open Source Code | Yes | The data can be found at [INE20], and code for pre-processing the data and the implementation of Fair Projection can be found at https://github.com/Hsiang Hsu/Fair-Projection. |
| Open Datasets | Yes | We also evaluate Fair Projection on a dataset derived from open and anonymized data from Brazil s national high school exam the Exame Nacional do Ensino Médio (ENEM) with over 1 million samples. ... The data can be found at [INE20], and code for pre-processing the data and the implementation of Fair Projection can be found at https://github.com/Hsiang Hsu/Fair-Projection. |
| Dataset Splits | Yes | All values reported in this section are from the test set with 70/30 train-test split. |
| Hardware Specification | Yes | These experiments were run on a machine with AMD Ryzen 2990WX 64-thread 32-Core CPU and NVIDIA TITAN Xp 12-GB GPU. |
| Software Dependencies | No | The paper mentions using 'Scikit-learn' and 'Tensor Flow' for implementation, but does not specify any version numbers for these or other software components. |
| Experiment Setup | Yes | For Fair Projection (the constrained optimization in (6)), we use cross-entropy (Fair Projection-CE) and KL-divergence (Fair Projection-KL) as the loss function8... For consistency, we used the same fairness metric (MEO, α = 0.01), base classifier (GBM), and train/test split, and each number is the average of 2 repeated experiments. |