{"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/counterfactual-fairness","title":"Counterfactual Fairness","arxiv_id":"1703.06856","date":"2017-03-20","proceeding":"NeurIPS 2017 12","authors":["Matt J. Kusner","Joshua R. Loftus","Chris Russell","Ricardo Silva"],"abstract":"Machine learning can impact people with legal or ethical consequences when it\nis used to automate decisions in areas such as insurance, lending, hiring, and\npredictive policing. In many of these scenarios, previous decisions have been\nmade that are unfairly biased against certain subpopulations, for example those\nof a particular race, gender, or sexual orientation. Since this past data may\nbe biased, machine learning predictors must account for this to avoid\nperpetuating or creating discriminatory practices. In this paper, we develop a\nframework for modeling fairness using tools from causal inference. Our\ndefinition of counterfactual fairness captures the intuition that a decision is\nfair towards an individual if it is the same in (a) the actual world and (b) a\ncounterfactual world where the individual belonged to a different demographic\ngroup. We demonstrate our framework on a real-world problem of fair prediction\nof success in law school.","url_abs":"http://arxiv.org/abs/1703.06856v3","url_pdf":"http://arxiv.org/pdf/1703.06856v3.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":"counterfactual-fairness","repo_url":"https://github.com/Kaaii/CS7290_Fairness_Eval_Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"counterfactual-fairness","repo_url":"https://github.com/mkusner/counterfactual-fairness","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"counterfactual-fairness","repo_url":"https://github.com/yunhao-tech/Counterfactual-Fairness","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"causal-inference","task_name":"Causal Inference"},{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":null,"task_name":"counterfactual"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1703.06856","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}