Achieving Equalized Odds by Resampling Sensitive Attributes
Authors: Yaniv Romano, Stephen Bates, Emmanuel Candes
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the applicability and validity of the proposed framework both in regression and multi-class classification problems, reporting improved performance over state-of-the-art methods. |
| Researcher Affiliation | Academia | Yaniv Romano Department of Statistics Stanford University Stanford, CA, USA yromano@stanford.edu Stephen Bates Department of Statistics Stanford University Stanford, CA, USA stephenbates@stanford.edu Emmanuel J. Candès Departments of Mathematics and of Statistics Stanford University Stanford, CA, USA candes@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Fair Dummies Model Fitting |
| Open Source Code | Yes | The software is available online at https://github.com/yromano/fair_dummies. |
| Open Datasets | Yes | We begin with experiments on two data sets with real-valued responses: the 2016 Medical Expenditure Panel Survey (MEPS), where we seek to predict medical usage based on demographic variables, and the widely used UCI Communities and Crime data set, where we seek to predict violent crime levels from census and police data. See Supplementary Section S5.1 for more details. |
| Dataset Splits | Yes | In all experiments, we randomly split the data into a training set (60%), a hold-out set (20%) to fit the test statistic for the fair-dummies test, and a test set (20%) to evaluate their performance. |
| Hardware Specification | No | The paper does not specify any particular hardware details (e.g., CPU, GPU models, or cloud computing resources) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names like PyTorch or TensorFlow with their respective versions) needed to replicate the experiments. |
| Experiment Setup | No | The paper states: 'Therefore, we choose to tune the set of parameters of each method only once and treat the chosen set as fixed in future experiments; see Supplementary Section S6.1 for a full description of the tuning of each method.' This indicates that specific experimental setup details, such as hyperparameters, are deferred to the supplementary material and are not explicitly provided in the main text. |