{"url":"/dataset/difair","name":"DiFair","full_name":null,"description_markdown":"DiFair serves as a meticulous endeavor to address the oversight in evaluating the impact of bias mitigation on useful gender knowledge while assessing gender neutrality in pretrained language models. This metric delves into not only quantifying a model's biased tendencies but also assessing the preservation of useful gender knowledge.","description_withheld":null,"homepage":"https://github.com/mzakizadeh/difair_public","introduced_date":"2023-10-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/difair-a-benchmark-for-disentangled","title":"DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and Bias","first_author":"Mahdi Zakizadeh","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Masked Language Modeling","url":"/task/masked-language-modeling","datasets_with_task":"/datasets/task/masked-language-modeling"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["DiFair"],"data_loaders":[],"num_papers_in_archive":3,"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."}