{"url":"/dataset/ci-mnist","name":"CI-MNIST","full_name":"Correlated and Imbalanced MNIST","description_markdown":"**CI-MNIST** (Correlated and Imbalanced MNIST) is a variant of [MNIST](/dataset/mnist) dataset with introduced different types of correlations between attributes, dataset features, and an artificial eligibility criterion. For an input image $x$, the label $y \\in \\\\{1, 0\\\\}$ indicates eligibility or ineligibility, respectively, given that $x$ is even or odd. The dataset defines the background colors as the protected or sensitive attribute $s \\in \\\\{0, 1\\\\}$, where blue denotes the unprivileged group and red denotes the privileged group. The dataset was designed in order to evaluate bias-mitigation approaches in challenging setups and be capable of controlling different dataset configurations.","description_withheld":null,"homepage":"https://openreview.net/pdf?id=OTnqQUEwPKu","introduced_date":"2021-06-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-bias-mitigation-algorithms-in","title":"Benchmarking Bias Mitigation Algorithms in Representation Learning through Fairness Metrics","first_author":"Charan Reddy","url":null},"license":{"name":"Open","url":"https://github.com/charan223/FairDeepLearning/blob/main/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Fairness","url":"/task/fairness","datasets_with_task":"/datasets/task/fairness"},{"name":"Bias Detection","url":"/task/bias-detection","datasets_with_task":"/datasets/task/bias-detection"},{"name":"Age And Gender Classification","url":"/task/age-and-gender-classification","datasets_with_task":"/datasets/task/age-and-gender-classification"},{"name":"Gender Bias Detection","url":"/task/gender-bias-detection","datasets_with_task":"/datasets/task/gender-bias-detection"},{"name":"imbalanced classification","url":"/task/imbalanced-classification","datasets_with_task":"/datasets/task/imbalanced-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CI-MNIST"],"data_loaders":[{"repo":"https://github.com/charan223/FairDeepLearning","url":"https://github.com/charan223/FairDeepLearning/blob/main/README.md","frameworks":["pytorch"]}],"num_papers_in_archive":4,"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."}