An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning
Authors: Cyrus Cousins
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental Validation Figure 1 presents a brief experiment on the lauded adult dataset, where the task is to predict whether income is above or below $50k/yr. We train p( ; w)-minimizing SVM, and find significant risk-variation between groups; generally low risk for the white and Asian groups, and high risk for the native American and other groups. |
| Researcher Affiliation | Academia | Cyrus Cousins Department of Computer Science Brown University cyrus_cousins@brown.edu |
| Pseudocode | Yes | Algorithm 1 Approximate Empirical Malfare Minimization via the Projected Subgradient Method |
| Open Source Code | No | The paper mentions 'assistance with the experimental code' in acknowledgments but does not provide an explicit statement or link to the source code for the described methodology. |
| Open Datasets | Yes | Experimental Validation Figure 1 presents a brief experiment on the lauded adult dataset, where the task is to predict whether income is above or below $50k/yr. We train p( ; w)-minimizing SVM, and find significant risk-variation between groups; generally low risk for the white and Asian groups, and high risk for the native American and other groups. The experimental setup is detailed in appendix A.1. [11] Dheeru Dua and Casey Graff. UCI machine learning repository, 2021. URL http://archive. ics.uci.edu/ml. |
| Dataset Splits | No | The paper discusses training and test performance but does not specify details on validation splits (e.g., percentages, counts, or k-fold cross-validation). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. The paper only refers to models like 'hinge-loss SVM' and 'logistic regressors'. |
| Experiment Setup | No | The paper states 'The experimental setup is detailed in appendix A.1.' but does not provide specific hyperparameter values or training configurations in the main text. |