Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
Authors: Alekh Agarwal, Miroslav Dudik, Zhiwei Steven Wu
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we evaluate our method on several standard datasets, on the tasks of least-squares and logistic regression under statistical parity, with linear and tree-ensemble learners, and compare it with the unconstrained baselines as well as the technique of Johnson et al. (2016). Our method uncovers fairness accuracy frontiers and provides the first systematic scheme for enforcing fairness in a significantly broader class of learning problems than prior work. |
| Researcher Affiliation | Collaboration | 1Microsoft Research, Redmond, WA 2Microsoft Research, New York, NY 3University of Minnesota, Minneapolis, MN. |
| Pseudocode | Yes | Algorithm 1 Fair regression with statistical parity; Algorithm 2 Fair regression with Bounded Group Loss |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for its described methodology. |
| Open Datasets | Yes | Adult: The adult income dataset (Lichman, 2013); Law school: Law School Admissions Council s National Longitudinal Bar Passage Study (Wightman, 1998); Communities & crime: The dataset contains socio-economic, law enforcement, and crime data about communities in the US (Redmond & Baveja, 2002) |
| Dataset Splits | No | Thus we ended up with a total of five datasets, and split each into 50% for training and 50% for testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments. |
| Software Dependencies | No | We considered two variants of LS and LR oracles: linear learners from scikit-learn (Pedregosa et al., 2011), and tree ensembles from XGBoost (Chen & Guestrin, 2016). The paper mentions software by name but does not provide version numbers. |
| Experiment Setup | Yes | We ran Algorithm 1 on each training set over a range of constraint slack values ˆ", with a fixed discretization grid of size 40: Z = {1/40, 2/40, . . . , 1}. |