Retiring Adult: New Datasets for Fair Machine Learning

• URL: http://arxiv.org/abs/2108.04884v1
• Date: Tue, 10 Aug 2021 19:19:41 GMT
• Title: Retiring Adult: New Datasets for Fair Machine Learning
• Authors: Frances Ding, Moritz Hardt, John Miller, Ludwig Schmidt
• Abstract summary: UCI Adult has served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. Our primary contribution is a suite of new datasets that extend the existing data ecosystem for research on fair machine learning.
• Score: 47.27417042497261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US Census survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to study temporal shift and geographic variation. 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. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions. Our datasets are available at https://github.com/zykls/folktables.

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