Retiring Adult: New Datasets for Fair Machine Learning
Authors: Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. |
| Researcher Affiliation | Collaboration | Frances Ding UC Berkeley Moritz Hardt UC Berkeley John Miller UC Berkeley Ludwig Schmidt Toyota Research Institute |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The datasets and Python package are available for download at https://github.com/zykls/ folktables. |
| Open Datasets | Yes | The datasets and Python package are available for download at https://github.com/zykls/ folktables. |
| Dataset Splits | Yes | The UCI Machine Learning Repository contributed to this development by providing researchers with numerous datasets each with a fixed training and testing split. ... Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] Please see Appendix C. |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] Please see Appendix C. |
| Software Dependencies | No | The paper mentions a 'Python package called folktables' and various machine learning methods, but does not specify key software components with their version numbers required for reproduction. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] Please see Appendix C. |