{"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/intersectionality-multiple-group-fairness-in","title":"Intersectionality: Multiple Group Fairness in Expectation Constraints","arxiv_id":"1811.09960","date":"2018-11-25","proceeding":null,"authors":["Jack Fitzsimons","Michael Osborne","Stephen Roberts"],"abstract":"Group fairness is an important concern for machine learning researchers,\ndevelopers, and regulators. However, the strictness to which models must be\nconstrained to be considered fair is still under debate. The focus of this work\nis on constraining the expected outcome of subpopulations in kernel regression\nand, in particular, decision tree regression, with application to random\nforests, boosted trees and other ensemble models. While individual constraints\nwere previously addressed, this work addresses concerns about incorporating\nmultiple constraints simultaneously. The proposed solution does not affect the\norder of computational or memory complexity of the decision trees and is easily\nintegrated into models post training.","url_abs":"http://arxiv.org/abs/1811.09960v1","url_pdf":"http://arxiv.org/pdf/1811.09960v1.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":"intersectionality-multiple-group-fairness-in","repo_url":"https://github.com/OxfordML/Fair_Regression","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}