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..
A Reductions Approach to Fair Classification
Authors: Alekh Agarwal, Alina Beygelzimer, Miroslav Dudik, John Langford, Hanna Wallach
ICML 2018 | Venue PDF | 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. |