{"id":"https://openalex.org/W4402353896","doi":"https://doi.org/10.1109/ijcnn60899.2024.10649973","title":"Achieving Fairness through Constrained Recourse","display_name":"Achieving Fairness through Constrained Recourse","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402353896","doi":"https://doi.org/10.1109/ijcnn60899.2024.10649973"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10649973","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10649973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018562736","display_name":"Shuang Wang","orcid":"https://orcid.org/0000-0002-9809-4062"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuang Wang","raw_affiliation_strings":["Clemson University,Electrical and Computer Engineering,Clemson,SC,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100731120","display_name":"Yongkai Wu","orcid":"https://orcid.org/0000-0002-7313-9439"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yongkai Wu","raw_affiliation_strings":["Clemson University,Electrical and Computer Engineering,Clemson,SC,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17215851,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9991999864578247,"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":0.9991999864578247,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.991599977016449,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9722999930381775,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6157792210578918},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4291272759437561},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1448485553264618}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6157792210578918},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4291272759437561},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1448485553264618}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10649973","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10649973","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W2026019770","https://openalex.org/W2040825624","https://openalex.org/W2157928966","https://openalex.org/W2295598076","https://openalex.org/W2550080458","https://openalex.org/W2550530154","https://openalex.org/W2561435227","https://openalex.org/W2891340972","https://openalex.org/W2911765495","https://openalex.org/W2945295328","https://openalex.org/W2973319951","https://openalex.org/W2974168418","https://openalex.org/W3001400233","https://openalex.org/W3028820554","https://openalex.org/W3101038122","https://openalex.org/W3103795814","https://openalex.org/W3104149808","https://openalex.org/W3135487809","https://openalex.org/W3137991047","https://openalex.org/W3181414820","https://openalex.org/W4226290207","https://openalex.org/W4288758404","https://openalex.org/W4289258088","https://openalex.org/W4297812478","https://openalex.org/W4301861531","https://openalex.org/W4385482700","https://openalex.org/W6728551298","https://openalex.org/W6734014246","https://openalex.org/W6763290930","https://openalex.org/W6766498723","https://openalex.org/W6767069965","https://openalex.org/W6783998556","https://openalex.org/W6800374573","https://openalex.org/W6926258385"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Data-driven":[0],"decision-making":[1,31],"systems":[2],"are":[3],"progressively":[4],"deployed":[5],"in":[6,78,118,127],"high-risk":[7],"scenarios,":[8],"raising":[9],"significant":[10],"social":[11],"concerns":[12],"about":[13],"their":[14],"potential":[15,117],"to":[16,20,71,85],"perpetuate":[17],"inequalities":[18],"related":[19],"demographic":[21],"characteristics.":[22],"Recent":[23],"research":[24],"efforts":[25],"have":[26],"focused":[27],"on":[28,47],"ensuring":[29,119],"equal":[30],"for":[32],"individuals,":[33],"primarily":[34],"through":[35],"model":[36],"adjustments":[37],"and":[38,122,125],"data":[39],"modification":[40],"techniques.":[41],"However,":[42],"these":[43],"approaches":[44],"often":[45],"rely":[46],"distance-based":[48,110],"formulation,":[49],"overlooking":[50],"the":[51,73,103],"practical":[52,95],"aspects":[53],"of":[54,75,105],"achieving":[55],"equity.":[56],"To":[57],"address":[58],"this":[59],"gap,":[60],"our":[61,106,115],"study":[62],"introduces":[63],"a":[64],"novel":[65],"method":[66,81],"that":[67,101],"leverages":[68,82],"actionable":[69],"recourse":[70],"reflect":[72],"feasibility":[74,124],"attaining":[76],"fairness":[77,87],"decision-making.":[79],"This":[80],"constrained":[83],"optimization":[84],"achieve":[86],"within":[88],"limited":[89],"budgets,":[90],"thereby":[91],"balancing":[92],"equity":[93],"with":[94],"constraints.":[96],"We":[97],"present":[98],"experimental":[99],"results":[100,113],"demonstrate":[102],"superiority":[104],"approach":[107],"over":[108],"traditional":[109],"methods.":[111],"These":[112],"underscore":[114],"method's":[116],"equitable":[120],"decisions":[121],"maintaining":[123],"efficiency":[126],"real-world":[128],"applications.":[129]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}