{"id":"https://openalex.org/W4224329622","doi":"https://doi.org/10.1609/aaai.v36i9.21188","title":"Achieving Long-Term Fairness in Sequential Decision Making","display_name":"Achieving Long-Term Fairness in Sequential Decision Making","publication_year":2022,"publication_date":"2022-06-28","ids":{"openalex":"https://openalex.org/W4224329622","doi":"https://doi.org/10.1609/aaai.v36i9.21188"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v36i9.21188","doi":"https://doi.org/10.1609/aaai.v36i9.21188","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v36i9.21188","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v36i9.21188","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Yaowei Hu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yaowei Hu","raw_affiliation_strings":["University of Arkansas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arkansas","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100388671","display_name":"Lu Zhang","orcid":"https://orcid.org/0000-0002-8972-8799","observed_orcids":["https://orcid.org/0000-0002-8972-8799","https://orcid.org/0000-0002-3195-4291","https://orcid.org/0009-0008-9549-4731"]},"institutions":[{"id":"https://openalex.org/I78715868","display_name":"University of Arkansas at Fayetteville","ror":"https://ror.org/05jbt9m15","country_code":"US","type":"education","lineage":["https://openalex.org/I78715868"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lu Zhang","raw_affiliation_strings":["University of Arkansas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arkansas","institution_ids":["https://openalex.org/I78715868"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78715868"],"apc_list":null,"apc_paid":null,"fwci":3.4781,"has_fulltext":true,"cited_by_count":16,"citation_normalized_percentile":{"value":0.92446329,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"36","issue":"9","first_page":"9549","last_page":"9557"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9297000169754028,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9297000169754028,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.014000000432133675,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.005100000184029341,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/long-term-fairness","display_name":"long-term fairness","score":0.8659999966621399},{"id":"https://openalex.org/keywords/sequential-decision-making","display_name":"sequential decision making","score":0.9409999847412109},{"id":"https://openalex.org/keywords/path-specific-effects","display_name":"path-specific effects","score":0.7730000019073486},{"id":"https://openalex.org/keywords/constrained-optimization","display_name":"constrained optimization","score":0.8479999899864197},{"id":"https://openalex.org/keywords/temporal-fairness","display_name":"temporal fairness","score":0.5360000133514404},{"id":"https://openalex.org/keywords/convergence-analysis","display_name":"convergence analysis","score":0.656000018119812}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7032010555267334},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6699357032775879},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5950879454612732},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5737033486366272},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.5021572113037109},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.46301883459091187},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4345400333404541},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24900773167610168},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19415554404258728},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.08231344819068909},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1609/aaai.v36i9.21188","doi":"https://doi.org/10.1609/aaai.v36i9.21188","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v36i9.21188","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2204.01819","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2204.01819","pdf_url":"https://arxiv.org/pdf/2204.01819","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ojs.aaai.org:article/21188","doi":null,"is_oa":true,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/21188","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/21188/20937","source":{"id":"https://openalex.org/S4306400155","display_name":"AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI))","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2801652627","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/I2801652627"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.48550/arxiv.2204.01819","doi":"https://doi.org/10.48550/arxiv.2204.01819","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2204.01819","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1609/aaai.v36i9.21188","doi":"https://doi.org/10.1609/aaai.v36i9.21188","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v36i9.21188","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-2","norway-1"],"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, justice, and strong institutions","score":0.8100000023841858}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1411364616","display_name":"III:Small: Counterfactually Fair Machine Learning through Causal Modeling","funder_award_id":"1910284","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4156043112","display_name":"RII Track-1: Data Analytics that are Robust and Trusted (DART): From Smart Curation to Socially Aware Decision Making","funder_award_id":"1946391","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6910641553","display_name":"RII Track 2 FEC: Multi-Scale Integrative Approach to Digital Health: Collaborative Research and Education in Smart Health in West Virginia and Arkansas","funder_award_id":"1920920","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2085988980","https://openalex.org/W2098952447","https://openalex.org/W2162670686","https://openalex.org/W2290452516","https://openalex.org/W2530395818","https://openalex.org/W2550080458","https://openalex.org/W2550530154","https://openalex.org/W2593670774","https://openalex.org/W2617781997","https://openalex.org/W2752326903","https://openalex.org/W2773444500","https://openalex.org/W2790697880","https://openalex.org/W2884025810","https://openalex.org/W2889169527","https://openalex.org/W2905213372","https://openalex.org/W2911765495","https://openalex.org/W2949979136","https://openalex.org/W2952517774","https://openalex.org/W2962922829","https://openalex.org/W2962977061","https://openalex.org/W2962989257","https://openalex.org/W2970971581","https://openalex.org/W2998275867","https://openalex.org/W2999114164","https://openalex.org/W3003189938","https://openalex.org/W3009541669","https://openalex.org/W3034367601","https://openalex.org/W3034959582","https://openalex.org/W3036859210","https://openalex.org/W3092541244","https://openalex.org/W3100657402","https://openalex.org/W3101150805","https://openalex.org/W3116873649","https://openalex.org/W3157622669","https://openalex.org/W4287635639","https://openalex.org/W7021110359"],"related_works":["https://openalex.org/W7127289327","https://openalex.org/W4386554971","https://openalex.org/W4389599270","https://openalex.org/W4393157465","https://openalex.org/W7045284607","https://openalex.org/W7062444266","https://openalex.org/W4399365258","https://openalex.org/W4403488156","https://openalex.org/W2999114164","https://openalex.org/W7014508261"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,22],"propose":[4,23],"a":[5,34,52,81],"framework":[6],"for":[7,37,92],"achieving":[8],"long-term":[9,39,64],"fair":[10,44],"sequential":[11,45],"decision":[12,46],"making.":[13],"By":[14],"conducting":[15],"both":[16],"the":[17,29,57,60,63,96,107,110],"hard":[18],"and":[19,62,65,95,115],"soft":[20],"interventions,":[21],"to":[24,80],"take":[25],"path-specific":[26],"effects":[27],"on":[28,113],"time-lagged":[30],"causal":[31],"graph":[32],"as":[33,51,59,68],"quantitative":[35],"tool":[36],"measuring":[38],"fairness.":[40],"The":[41,103],"problem":[42,55,76],"of":[43,98,109],"making":[47],"is":[48,90,100],"then":[49],"formulated":[50],"constrained":[53],"optimization":[54,75],"with":[56],"utility":[58],"objective":[61],"short-term":[66],"fairness":[67],"constraints.":[69],"We":[70],"show":[71],"that":[72],"such":[73],"an":[74],"can":[77],"be":[78],"converted":[79],"performative":[82],"risk":[83,87],"optimization.":[84],"Finally,":[85],"repeated":[86],"minimization":[88],"(RRM)":[89],"used":[91],"model":[93],"training,":[94],"convergence":[97],"RRM":[99],"theoretically":[101],"analyzed.":[102],"empirical":[104],"evaluation":[105],"shows":[106],"effectiveness":[108],"proposed":[111],"algorithm":[112],"synthetic":[114],"semi-synthetic":[116],"temporal":[117],"datasets.":[118]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2022-04-26T00:00:00"}