{"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/achieving-long-term-fairness-in-sequential","title":"Achieving Long-Term Fairness in Sequential Decision Making","arxiv_id":"2204.01819","date":"2022-04-04","proceeding":null,"authors":["Yaowei Hu","Lu Zhang"],"abstract":"In this paper, we propose a framework for achieving long-term fair sequential decision making. By conducting both the hard and soft interventions, we propose to take path-specific effects on the time-lagged causal graph as a quantitative tool for measuring long-term fairness. The problem of fair sequential decision making is then formulated as a constrained optimization problem with the utility as the objective and the long-term and short-term fairness as constraints. We show that such an optimization problem can be converted to a performative risk optimization. Finally, repeated risk minimization (RRM) is used for model training, and the convergence of RRM is theoretically analyzed. The empirical evaluation shows the effectiveness of the proposed algorithm on synthetic and semi-synthetic temporal datasets.","url_abs":"https://arxiv.org/abs/2204.01819v1","url_pdf":"https://arxiv.org/pdf/2204.01819v1.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":"achieving-long-term-fairness-in-sequential","repo_url":"https://github.com/yaoweihu/achieving-long-term-fairness","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"fairness","task_name":"Fairness"},{"task_slug":"sequential-decision-making","task_name":"Sequential Decision Making"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2204.01819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.01819"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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":"deterministic:regex_extraction","url":"https://github.com/yaoweihu/Achieving-Long-term-Fairness","reach":null}],"summary":{"ran":1,"ran_fixture":1,"ran_honours":3},"by_repo_kind":{"official":{"samples":5,"ran":5,"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":5,"samples":[{"code_sha256_prefix":"54b136b0c7ab1398","entry":"FairModel","repo":"yaoweihu/Achieving-Long-term-Fairness","repo_kind":"official","path":"src/fair_model.py","file_url":"https://github.com/yaoweihu/Achieving-Long-term-Fairness/blob/HEAD/src/fair_model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"54b136b0c7ab1398"}},{"code_sha256_prefix":"5d0165191a123dd1","entry":"combine_featuers","repo":"yaoweihu/Achieving-Long-term-Fairness","repo_kind":"official","path":"src/fair_model.py","file_url":"https://github.com/yaoweihu/Achieving-Long-term-Fairness/blob/HEAD/src/fair_model.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"5d0165191a123dd1"}},{"code_sha256_prefix":"d47d2823041d8099","entry":"logistic","repo":"yaoweihu/Achieving-Long-term-Fairness","repo_kind":"official","path":"src/fair_model.py","file_url":"https://github.com/yaoweihu/Achieving-Long-term-Fairness/blob/HEAD/src/fair_model.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d47d2823041d8099"}},{"code_sha256_prefix":"dcc242b0ed171083","entry":"to_numpy","repo":"yaoweihu/Achieving-Long-term-Fairness","repo_kind":"official","path":"src/fair_model.py","file_url":"https://github.com/yaoweihu/Achieving-Long-term-Fairness/blob/HEAD/src/fair_model.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"dcc242b0ed171083"}},{"code_sha256_prefix":"2e668cc2d11da9e1","entry":"to_tensor","repo":"yaoweihu/Achieving-Long-term-Fairness","repo_kind":"official","path":"src/fair_model.py","file_url":"https://github.com/yaoweihu/Achieving-Long-term-Fairness/blob/HEAD/src/fair_model.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2e668cc2d11da9e1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}