{"id":"https://openalex.org/W3171743261","doi":"https://doi.org/10.1145/3447548.3467258","title":"Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition","display_name":"Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition","publication_year":2021,"publication_date":"2021-08-12","ids":{"openalex":"https://openalex.org/W3171743261","doi":"https://doi.org/10.1145/3447548.3467258","mag":"3171743261"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467258","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467258","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","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/A5068293793","display_name":"Weishen Pan","orcid":"https://orcid.org/0000-0002-3274-5037"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weishen Pan","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069563876","display_name":"Sen Cui","orcid":"https://orcid.org/0000-0003-1224-5569"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Cui","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030951014","display_name":"Jiang Bian","orcid":"https://orcid.org/0000-0002-2238-5429"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiang Bian","raw_affiliation_strings":["University of Florida, Gainesville, FL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Florida, Gainesville, FL, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065063835","display_name":"Changshui Zhang","orcid":"https://orcid.org/0000-0002-8088-367X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changshui Zhang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455768","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0001-9459-9461"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Cornell University, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University, New York, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.1116,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.9548819,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1287","last_page":"1297"},"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":0.9983999729156494,"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":0.9983999729156494,"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":0.9918000102043152,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.958299994468689,"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":0.7277538776397705},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5488359332084656},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5255267024040222},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5210363268852234},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5195612907409668},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5165611505508423},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.4965894818305969},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.4844253361225128},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47988665103912354},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.45014411211013794},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4424896836280823},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.42837584018707275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4282756447792053},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3625807762145996},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16013503074645996},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08544087409973145}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7277538776397705},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5488359332084656},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5255267024040222},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5210363268852234},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5195612907409668},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5165611505508423},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.4965894818305969},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.4844253361225128},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47988665103912354},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.45014411211013794},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4424896836280823},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.42837584018707275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4282756447792053},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3625807762145996},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16013503074645996},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08544087409973145},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467258","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467258","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7341942736","display_name":null,"funder_award_id":"2018AAA0100701","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W199682464","https://openalex.org/W1524326598","https://openalex.org/W2100960835","https://openalex.org/W2295598076","https://openalex.org/W2540757487","https://openalex.org/W2618851150","https://openalex.org/W2753845591","https://openalex.org/W2788416960","https://openalex.org/W2790744245","https://openalex.org/W2904539038","https://openalex.org/W2905213372","https://openalex.org/W2962977061","https://openalex.org/W2963116854","https://openalex.org/W2964060106","https://openalex.org/W2981358273","https://openalex.org/W3036616490","https://openalex.org/W3041140240","https://openalex.org/W3091210704","https://openalex.org/W3100573319","https://openalex.org/W3102440254","https://openalex.org/W3102476541","https://openalex.org/W3120740533","https://openalex.org/W4212774754","https://openalex.org/W4289258088","https://openalex.org/W4391795131"],"related_works":["https://openalex.org/W4255837520","https://openalex.org/W2387011115","https://openalex.org/W4234808182","https://openalex.org/W2382043075","https://openalex.org/W2809151339","https://openalex.org/W2360673138","https://openalex.org/W2809370583","https://openalex.org/W2333722679","https://openalex.org/W4255628145","https://openalex.org/W2093320919"],"abstract_inverted_index":{"Algorithmic":[0],"fairness":[1],"has":[2,19],"aroused":[3],"considerable":[4],"interests":[5],"in":[6],"data":[7,153],"mining":[8],"and":[9,37,82,107,137,151,165],"machine":[10],"learning":[11],"communities":[12],"recently.":[13],"So":[14],"far":[15],"the":[16,24,41,55,60,76,89,92,104,108,112,117,120,169],"existing":[17,66],"research":[18],"been":[20],"mostly":[21],"focusing":[22],"on":[23,111,122,148],"development":[25],"of":[26,57,59,62,94,142],"quantitative":[27,143],"metrics":[28],"to":[29,44,53,87,139,157,168],"measure":[30],"algorithm":[31,42],"disparities":[32],"across":[33],"different":[34],"protected":[35],"groups,":[36],"approaches":[38],"for":[39],"adjusting":[40],"output":[43],"reduce":[45],"such":[46],"disparities.":[47,64,171],"In":[48],"this":[49],"paper,":[50],"we":[51,74],"propose":[52,83],"study":[54],"problem":[56],"identification":[58],"source":[61],"model":[63,135,170],"Unlike":[65],"interpretation":[67],"methods":[68],"which":[69,100],"typically":[70],"learn":[71],"feature":[72,80],"importance,":[73],"consider":[75,116],"causal":[77,98],"relationships":[78],"among":[79],"variables":[81],"a":[84,140],"novel":[85],"framework":[86,132],"decompose":[88],"disparity":[90,144],"into":[91],"sum":[93],"contributions":[95],"from":[96],"fairness-aware":[97],"paths,":[99],"are":[101,155],"paths":[102,127],"linking":[103],"sensitive":[105],"attribute":[106],"final":[109],"predictions,":[110],"graph.":[113],"We":[114],"also":[115,134],"scenario":[118],"when":[119],"directions":[121],"certain":[123],"edges":[124],"within":[125],"those":[126],"cannot":[128],"be":[129],"determined.":[130],"Our":[131],"is":[133],"agnostic":[136],"applicable":[138],"variety":[141],"measures.":[145],"Empirical":[146],"evaluations":[147],"both":[149],"synthetic":[150],"real-world":[152],"sets":[154],"provided":[156],"show":[158],"that":[159],"our":[160],"method":[161],"can":[162],"provide":[163],"precise":[164],"comprehensive":[166],"explanations":[167]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}