{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optimized-data-pre-processing-for","title":"Optimized Data Pre-Processing for Discrimination Prevention","arxiv_id":"1704.03354","date":"2017-04-11","proceeding":null,"authors":["Flavio P. Calmon","Dennis Wei","Karthikeyan Natesan Ramamurthy","Kush R. Varshney"],"abstract":"Non-discrimination is a recognized objective in algorithmic decision making.\nIn this paper, we introduce a novel probabilistic formulation of data\npre-processing for reducing discrimination. We propose a convex optimization\nfor learning a data transformation with three goals: controlling\ndiscrimination, limiting distortion in individual data samples, and preserving\nutility. We characterize the impact of limited sample size in accomplishing\nthis objective, and apply two instances of the proposed optimization to\ndatasets, including one on real-world criminal recidivism. The results\ndemonstrate that all three criteria can be simultaneously achieved and also\nreveal interesting patterns of bias in American society.","url_abs":"http://arxiv.org/abs/1704.03354v1","url_pdf":"http://arxiv.org/pdf/1704.03354v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"optimized-data-pre-processing-for","repo_url":"https://github.com/fair-preprocessing/nips2017","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}