{"url":"/dataset/acspubliccoverage","name":"ACSPublicCoverage","full_name":null,"description_markdown":"ACSPublicCoverage: predict whether an individual is covered by public health insurance, after filtering the ACS PUMS data sample to only include individuals under the age of 65, and those with an income of less than $30,000. This filtering focuses the prediction problem on low-income individuals who are not eligible for Medicare.","description_withheld":null,"homepage":"https://github.com/socialfoundations/folktables","introduced_date":null,"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":[],"tasks":[],"languages":[],"variants":["ACSPublicCoverage"],"data_loaders":[],"num_papers_in_archive":15,"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."}