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..
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning
Authors: Cyrus Cousins
NeurIPS 2021 | Venue PDF | 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 EMAIL |
| 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. |