{"id":"https://openalex.org/W3215005267","doi":"https://doi.org/10.1145/3531146.3533126","title":"Fairness for AUC via Feature Augmentation","display_name":"Fairness for AUC via Feature Augmentation","publication_year":2022,"publication_date":"2022-06-20","ids":{"openalex":"https://openalex.org/W3215005267","doi":"https://doi.org/10.1145/3531146.3533126","mag":"3215005267"},"language":"en","primary_location":{"id":"doi:10.1145/3531146.3533126","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533126","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","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/A5052464509","display_name":"Hortense Fong","orcid":"https://orcid.org/0000-0003-0256-7943"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hortense Fong","raw_affiliation_strings":["Yale University, USA"],"affiliations":[{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100663903","display_name":"Vineet Kumar","orcid":"https://orcid.org/0000-0001-8784-6858"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vineet Kumar","raw_affiliation_strings":["Yale University, USA"],"affiliations":[{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065512381","display_name":"Anay Mehrotra","orcid":"https://orcid.org/0000-0002-8566-5452"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anay Mehrotra","raw_affiliation_strings":["Yale University, USA"],"affiliations":[{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063089732","display_name":"Nisheeth K. Vishnoi","orcid":"https://orcid.org/0000-0002-0255-1119"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nisheeth K. Vishnoi","raw_affiliation_strings":["Yale University, USA"],"affiliations":[{"raw_affiliation_string":"Yale University, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5052464509"],"corresponding_institution_ids":["https://openalex.org/I32971472"],"apc_list":null,"apc_paid":null,"fwci":0.3864,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65377532,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"610","last_page":"610"},"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":0.995199978351593,"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":0.995199978351593,"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":0.967199981212616,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.9466999769210815,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5838995575904846},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.5836284160614014},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.580698549747467},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5236613154411316},{"id":"https://openalex.org/keywords/disadvantaged","display_name":"Disadvantaged","score":0.5013244152069092},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4995574951171875},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48236650228500366},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44296708703041077},{"id":"https://openalex.org/keywords/area-under-curve","display_name":"Area under curve","score":0.4325900077819824},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.42170462012290955},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37836647033691406},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33964043855667114},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32371050119400024},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.0797375738620758}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5838995575904846},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5836284160614014},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.580698549747467},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5236613154411316},{"id":"https://openalex.org/C2780623907","wikidata":"https://www.wikidata.org/wiki/Q106394435","display_name":"Disadvantaged","level":2,"score":0.5013244152069092},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4995574951171875},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48236650228500366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44296708703041077},{"id":"https://openalex.org/C3020225094","wikidata":"https://www.wikidata.org/wiki/Q80091","display_name":"Area under curve","level":3,"score":0.4325900077819824},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.42170462012290955},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37836647033691406},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33964043855667114},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32371050119400024},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0797375738620758},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C112705442","wikidata":"https://www.wikidata.org/wiki/Q323936","display_name":"Pharmacokinetics","level":2,"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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3531146.3533126","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533126","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1998307574","https://openalex.org/W2014352947","https://openalex.org/W2042946827","https://openalex.org/W2048390639","https://openalex.org/W2100960835","https://openalex.org/W2116984840","https://openalex.org/W2120100126","https://openalex.org/W2158698691","https://openalex.org/W2162670686","https://openalex.org/W2302343385","https://openalex.org/W2530395818","https://openalex.org/W2540757487","https://openalex.org/W2550925836","https://openalex.org/W2599025709","https://openalex.org/W2778716829","https://openalex.org/W2885491668","https://openalex.org/W2885659818","https://openalex.org/W2911749777","https://openalex.org/W2963100392","https://openalex.org/W2963718755","https://openalex.org/W2963741226","https://openalex.org/W2964031043","https://openalex.org/W2964089577","https://openalex.org/W2964256806","https://openalex.org/W2971300654","https://openalex.org/W3034396216","https://openalex.org/W3099361686","https://openalex.org/W3101686649","https://openalex.org/W3105536512","https://openalex.org/W3123374861","https://openalex.org/W3209809080","https://openalex.org/W3211941956"],"related_works":["https://openalex.org/W2266938806","https://openalex.org/W3108466129","https://openalex.org/W2011314675","https://openalex.org/W2903708425","https://openalex.org/W3046905389","https://openalex.org/W4200065130","https://openalex.org/W2588799846","https://openalex.org/W4399212077","https://openalex.org/W2151785814","https://openalex.org/W4213184571"],"abstract_inverted_index":{"We":[0,69,129],"study":[1],"fairness":[2,103],"in":[3,57],"the":[4,9,14,17,21,44,71,85,92,146],"context":[5],"of":[6,20,73,95],"classification":[7],"where":[8],"performance":[10],"is":[11,26,61],"measured":[12],"by":[13],"area":[15],"under":[16],"curve":[18],"(AUC)":[19],"receiver":[22],"operating":[23],"characteristic.":[24],"AUC":[25,83,102,144,154],"commonly":[27],"used":[28],"when":[29],"both":[30],"Type":[31,36],"I":[32],"(false":[33,38],"positive)":[34],"and":[35,134,138,155],"II":[37],"negative)":[39],"errors":[40],"are":[41],"important.":[42],"However,":[43],"same":[45],"classifier":[46],"can":[47],"have":[48],"significantly":[49,142],"varying":[50],"AUCs":[51],"for":[52,84,145],"different":[53],"protected":[54],"groups":[55],"and,":[56],"real-world":[58,135],"applications,":[59],"it":[60,141],"often":[62],"desirable":[63],"to":[64,75,79,123,150],"reduce":[65],"such":[66],"cross-group":[67],"differences.":[68],"address":[70],"problem":[72],"how":[74],"select":[76],"additional":[77],"features":[78,96],"most":[80],"greatly":[81],"improve":[82],"disadvantaged":[86,147],"group.":[87],"Our":[88],"results":[89],"establish":[90],"that":[91,140],"unconditional":[93],"variance":[94,106],"does":[97],"not":[98],"inform":[99],"us":[100],"about":[101],"but":[104],"class-conditional":[105],"does.":[107],"Using":[108],"this":[109],"connection,":[110],"we":[111],"develop":[112],"a":[113],"novel":[114],"approach,":[115],"fairAUC,":[116],"based":[117],"on":[118,132],"feature":[119],"augmentation":[120],"(adding":[121],"features)":[122],"mitigate":[124],"bias":[125,157],"between":[126,158],"identifiable":[127],"groups.":[128,159],"evaluate":[130],"fairAUC":[131],"synthetic":[133],"(COMPAS)":[136],"datasets":[137],"find":[139],"improves":[143],"group":[148],"relative":[149],"benchmarks":[151],"maximizing":[152],"overall":[153],"minimizing":[156]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}