Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 Mรฉdio (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, ฮฑ = 0.01), base classifier (GBM), and train/test split, and each number is the average of 2 repeated experiments. |