{"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/a-convex-framework-for-fair-regression","title":"A Convex Framework for Fair Regression","arxiv_id":"1706.02409","date":"2017-06-07","proceeding":null,"authors":["Richard Berk","Hoda Heidari","Shahin Jabbari","Matthew Joseph","Michael Kearns","Jamie Morgenstern","Seth Neel","Aaron Roth"],"abstract":"We introduce a flexible family of fairness regularizers for (linear and\nlogistic) regression problems. These regularizers all enjoy convexity,\npermitting fast optimization, and they span the rang from notions of group\nfairness to strong individual fairness. By varying the weight on the fairness\nregularizer, we can compute the efficient frontier of the accuracy-fairness\ntrade-off on any given dataset, and we measure the severity of this trade-off\nvia a numerical quantity we call the Price of Fairness (PoF). The centerpiece\nof our results is an extensive comparative study of the PoF across six\ndifferent datasets in which fairness is a primary consideration.","url_abs":"http://arxiv.org/abs/1706.02409v1","url_pdf":"http://arxiv.org/pdf/1706.02409v1.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":"a-convex-framework-for-fair-regression","repo_url":"https://github.com/hyungrok-do/fair-glm-cvx","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.02409","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}