{"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-risk-measures","title":"Fairness risk measures","arxiv_id":"1901.08665","date":"2019-01-24","proceeding":null,"authors":["Robert C. Williamson","Aditya Krishna Menon"],"abstract":"Ensuring that classifiers are non-discriminatory or fair with respect to a\nsensitive feature (e.g., race or gender) is a topical problem. Progress in this\ntask requires fixing a definition of fairness, and there have been several\nproposals in this regard over the past few years. Several of these, however,\nassume either binary sensitive features (thus precluding categorical or\nreal-valued sensitive groups), or result in non-convex objectives (thus\nadversely affecting the optimisation landscape). In this paper, we propose a\nnew definition of fairness that generalises some existing proposals, while\nallowing for generic sensitive features and resulting in a convex objective.\nThe key idea is to enforce that the expected losses (or risks) across each\nsubgroup induced by the sensitive feature are commensurate. We show how this\nrelates to the rich literature on risk measures from mathematical finance. As a\nspecial case, this leads to a new convex fairness-aware objective based on\nminimising the conditional value at risk (CVaR).","url_abs":"http://arxiv.org/abs/1901.08665v1","url_pdf":"http://arxiv.org/pdf/1901.08665v1.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-risk-measures","repo_url":"https://github.com/JohanvandenHeuvel/FairnessRiskMeasures","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"fairness","task_name":"Fairness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1901.08665","atlas_url":"https://app.syntology.ai/?focus=1901.08665","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.08665"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/JohanvandenHeuvel/FairnessRiskMeasures","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"listed":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"fb9b99e159fa4aee","entry":"load_adult","repo":"JohanvandenHeuvel/FairnessRiskMeasures","repo_kind":"listed","path":"load_data.py","file_url":"https://github.com/JohanvandenHeuvel/FairnessRiskMeasures/blob/HEAD/load_data.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"fb9b99e159fa4aee"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}