{"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/fairness-constraints-mechanisms-for-fair","title":"Fairness Constraints: Mechanisms for Fair Classification","arxiv_id":"1507.05259","date":"2015-07-19","proceeding":null,"authors":["Muhammad Bilal Zafar","Isabel Valera","Manuel Gomez Rodriguez","Krishna P. Gummadi"],"abstract":"Algorithmic decision making systems are ubiquitous across a wide variety of\nonline as well as offline services. These systems rely on complex learning\nmethods and vast amounts of data to optimize the service functionality,\nsatisfaction of the end user and profitability. However, there is a growing\nconcern that these automated decisions can lead, even in the absence of intent,\nto a lack of fairness, i.e., their outcomes can disproportionately hurt (or,\nbenefit) particular groups of people sharing one or more sensitive attributes\n(e.g., race, sex). In this paper, we introduce a flexible mechanism to design\nfair classifiers by leveraging a novel intuitive measure of decision boundary\n(un)fairness. We instantiate this mechanism with two well-known classifiers,\nlogistic regression and support vector machines, and show on real-world data\nthat our mechanism allows for a fine-grained control on the degree of fairness,\noften at a small cost in terms of accuracy.","url_abs":"http://arxiv.org/abs/1507.05259v5","url_pdf":"http://arxiv.org/pdf/1507.05259v5.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":"fairness-constraints-mechanisms-for-fair","repo_url":"https://github.com/mbilalzafar/fair-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"fairness-constraints-mechanisms-for-fair","repo_url":"https://github.com/wnstlr/FACT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1507.05259","atlas_url":"https://app.syntology.ai/?focus=1507.05259","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}