{"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/from-parity-to-preference-based-notions-of","title":"From Parity to Preference-based Notions of Fairness in Classification","arxiv_id":"1707.00010","date":"2017-06-30","proceeding":"NeurIPS 2017 12","authors":["Muhammad Bilal Zafar","Isabel Valera","Manuel Gomez Rodriguez","Krishna P. Gummadi","Adrian Weller"],"abstract":"The adoption of automated, data-driven decision making in an ever expanding\nrange of applications has raised concerns about its potential unfairness\ntowards certain social groups. In this context, a number of recent studies have\nfocused on defining, detecting, and removing unfairness from data-driven\ndecision systems. However, the existing notions of fairness, based on parity\n(equality) in treatment or outcomes for different social groups, tend to be\nquite stringent, limiting the overall decision making accuracy. In this paper,\nwe draw inspiration from the fair-division and envy-freeness literature in\neconomics and game theory and propose preference-based notions of fairness --\ngiven the choice between various sets of decision treatments or outcomes, any\ngroup of users would collectively prefer its treatment or outcomes, regardless\nof the (dis)parity as compared to the other groups. Then, we introduce\ntractable proxies to design margin-based classifiers that satisfy these\npreference-based notions of fairness. Finally, we experiment with a variety of\nsynthetic and real-world datasets and show that preference-based fairness\nallows for greater decision accuracy than parity-based fairness.","url_abs":"http://arxiv.org/abs/1707.00010v2","url_pdf":"http://arxiv.org/pdf/1707.00010v2.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":"from-parity-to-preference-based-notions-of","repo_url":"https://github.com/mbilalzafar/fair-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"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":{"atlas_url":"https://app.syntology.ai/?focus=1707.00010","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}