A Reductions Approach to Fair Classification
Authors: Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experimental Results We now examine how our exponentiated-gradient reduction performs at the task of binary classification subject to either demographic parity or equalized odds. We provide an evaluation of our grid-search reduction in Appendix D. We compared our reduction with the score-based postprocessing algorithm of Hardt et al. (2016)... We used four data sets, randomly splitting each one into training examples (75%) and test examples (25%): |
| Researcher Affiliation | Industry | 1Microsoft Research, New York 2Yahoo! Research, New York. |
| Pseudocode | Yes | Algorithm 1 Exp. gradient reduction for fair classification |
| Open Source Code | Yes | 5https://github.com/Microsoft/fairlearn |
| Open Datasets | Yes | The adult income data set (Lichman, 2013)... Pro Publica s COMPAS recidivism data... Law School Admissions Council s National Longitudinal Bar Passage Study (Wightman, 1998)... The Dutch census data set (Dutch Central Bureau for Statistics, 2001) |
| Dataset Splits | Yes | We used four data sets, randomly splitting each one into training examples (75%) and test examples (25%): |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'scikit-learn' as a base classifier implementation but does not specify a version number for this or any other software dependency. |
| Experiment Setup | Yes | We considered ε {0.001, . . . , 0.1} and for each value ran Algorithm 1 with bck = ε across all k. |