{"url":"/dataset/acs-pums","name":"ACS PUMS","full_name":null,"description_markdown":"**ACS PUMS** stands for American Community Survey (ACS) Public Use Microdata Sample (PUMS) and has been used to construct several tabular datasets for studying fairness in machine learning:\r\n\r\n- ACSIncome: to predict whether an individual’s income is above $50,000.\r\n\r\n- ACSPublicCoverage: to predict whether an individual is covered by public health insurance.\r\n\r\n- ACSMobility: to predict whether an individual had the same residential address one year ago.\r\n\r\n- ACSEmployment: to predict whether an individual is employed.\r\n\r\n- ACSTravelTime: predict whether an individual has a commute to work that is longer than 20 minutes.","description_withheld":null,"homepage":"https://github.com/zykls/folktables","introduced_date":"2021-08-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/retiring-adult-new-datasets-for-fair-machine","title":"Retiring Adult: New Datasets for Fair Machine Learning","first_author":"Frances Ding","url":null},"license":null,"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"Fairness","url":"/task/fairness","datasets_with_task":"/datasets/task/fairness"}],"languages":[],"variants":["ACS PUMS"],"data_loaders":[{"repo":"https://github.com/namkoong-lab/whyshift","url":"https://github.com/namkoong-lab/whyshift","frameworks":["pytorch"]}],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}