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 General Approach to Fairness with Optimal Transport
Authors: Chiappa Silvia, Jiang Ray, Stepleton Tom, Pacchiano Aldo, Jiang Heinrich, Aslanides John3633-3640
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate its performance on several benchmark fairness datasets. Overall, our methods reach higher fairness with lower loss in accuracy. |
| Researcher Affiliation | Collaboration | 1Deep Mind London, 2UC Berkeley, 3Google Research |
| Pseudocode | Yes | Algorithm 1 Wass-p Geodesic |
| Open Source Code | No | The paper does not provide any statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We evaluated our approach on the National Longitudinal Survey of Youth (NLSY) 1979 regression dataset, on the UCI Communities & Crime (C&C) (Lichman 2013) regression dataset, on the Law School Admission Council (LSAC) regression dataset, and on the UCI Adult binary classification dataset. |
| Dataset Splits | No | The paper mentions 'test performance' and training, but does not explicitly provide details on train/validation/test dataset splits (percentages, counts, or specific predefined splits) within the main text. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions tuning hyperparameters and defining loss functions, but does not provide specific values for hyperparameters or detailed training configurations in the main text. |