{"id":"https://openalex.org/W4283165383","doi":"https://doi.org/10.1145/3531146.3533225","title":"Fairness-aware Model-agnostic Positive and Unlabeled Learning","display_name":"Fairness-aware Model-agnostic Positive and Unlabeled Learning","publication_year":2022,"publication_date":"2022-06-20","ids":{"openalex":"https://openalex.org/W4283165383","doi":"https://doi.org/10.1145/3531146.3533225"},"language":"en","primary_location":{"id":"doi:10.1145/3531146.3533225","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533225","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2206.09346","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101794936","display_name":"Ziwei Wu","orcid":"https://orcid.org/0000-0003-3999-4367"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ziwei Wu","raw_affiliation_strings":["School of Information Sciences, University of Illinois at Urbana-Champaign, USA"],"affiliations":[{"raw_affiliation_string":"School of Information Sciences, University of Illinois at Urbana-Champaign, USA","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073158087","display_name":"Jingrui He","orcid":"https://orcid.org/0000-0002-6429-6272"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingrui He","raw_affiliation_strings":["School of Information Sciences, University of Illinois at Urbana-Champaign, USA"],"affiliations":[{"raw_affiliation_string":"School of Information Sciences, University of Illinois at Urbana-Champaign, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101794936"],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":0.6884,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.76079137,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1698","last_page":"1708"},"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":0.9984999895095825,"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":0.9984999895095825,"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":0.9879999756813049,"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":0.9781000018119812,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7408289313316345},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7394545674324036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.729072630405426},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.7230364084243774},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6488036513328552},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5606873035430908},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5148188471794128},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4585130214691162},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.44364020228385925},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10894560813903809},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.10840943455696106}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7408289313316345},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7394545674324036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.729072630405426},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.7230364084243774},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6488036513328552},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5606873035430908},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5148188471794128},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4585130214691162},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.44364020228385925},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10894560813903809},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.10840943455696106},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3531146.3533225","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533225","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"},{"id":"pmh:oai:arXiv.org:2206.09346","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.09346","pdf_url":"https://arxiv.org/pdf/2206.09346","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2206.09346","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.09346","pdf_url":"https://arxiv.org/pdf/2206.09346","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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"},"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1497683195","https://openalex.org/W1588170973","https://openalex.org/W1593505700","https://openalex.org/W1703735685","https://openalex.org/W1996437515","https://openalex.org/W2000288463","https://openalex.org/W2014352947","https://openalex.org/W2016317391","https://openalex.org/W2050871273","https://openalex.org/W2051731935","https://openalex.org/W2077373870","https://openalex.org/W2095932468","https://openalex.org/W2100960835","https://openalex.org/W2116520176","https://openalex.org/W2123958887","https://openalex.org/W2132442585","https://openalex.org/W2133227149","https://openalex.org/W2142246398","https://openalex.org/W2162670686","https://openalex.org/W2296988146","https://openalex.org/W2514278201","https://openalex.org/W2530395818","https://openalex.org/W2599025709","https://openalex.org/W2753845591","https://openalex.org/W2789970635","https://openalex.org/W2790025105","https://openalex.org/W2791059564","https://openalex.org/W2886654930","https://openalex.org/W2897978524","https://openalex.org/W2912497862","https://openalex.org/W2913504990","https://openalex.org/W2946133803","https://openalex.org/W2949200088","https://openalex.org/W2949487022","https://openalex.org/W2950029751","https://openalex.org/W2951910382","https://openalex.org/W2963341628","https://openalex.org/W2964031043","https://openalex.org/W2971251505","https://openalex.org/W2989887895","https://openalex.org/W3016227400","https://openalex.org/W3030081171","https://openalex.org/W3086641451","https://openalex.org/W3101215053","https://openalex.org/W3102942031","https://openalex.org/W3104165014","https://openalex.org/W3106004876","https://openalex.org/W3120740533","https://openalex.org/W3128289777","https://openalex.org/W4253828197","https://openalex.org/W4282813715","https://openalex.org/W4287997328","https://openalex.org/W4297663312","https://openalex.org/W4297795193","https://openalex.org/W4298005857","https://openalex.org/W4298846155","https://openalex.org/W4300457951"],"related_works":["https://openalex.org/W3204418343","https://openalex.org/W4292388283","https://openalex.org/W1560624709","https://openalex.org/W3214142563","https://openalex.org/W3166286441","https://openalex.org/W4221162086","https://openalex.org/W3111760155","https://openalex.org/W4382934300","https://openalex.org/W2121061354","https://openalex.org/W4285388059"],"abstract_inverted_index":{"With":[0],"the":[1,31,58,71,82,87,94,154,157,171,189,193,198],"increasing":[2],"application":[3],"of":[4,74,156,187],"machine":[5,79],"learning":[6,80],"in":[7,54,81,93,145,185,210],"high-stake":[8],"decision-making":[9],"problems,":[10],"potential":[11],"algorithmic":[12],"bias":[13],"towards":[14],"people":[15],"from":[16,130],"certain":[17],"social":[18],"groups":[19],"poses":[20],"negative":[21],"impacts":[22],"on":[23,77,153,197],"individuals":[24,129],"and":[25,39,49,84,97,141,174,192,200,213],"our":[26,149,206],"society":[27],"at":[28],"large.":[29],"In":[30,108,123],"real-world":[32,201],"scenario,":[33],"many":[34],"such":[35,42],"problems":[36],"involve":[37],"positive":[38,139,143,172],"unlabeled":[40,175],"data":[41,202],"as":[43,148],"medical":[44,55],"diagnosis,":[45,56],"criminal":[46],"risk":[47],"assessment":[48],"recommender":[50],"systems.":[51],"For":[52],"instance,":[53],"only":[57],"diagnosed":[59],"diseases":[60],"will":[61,67],"be":[62,182],"recorded":[63],"(positive)":[64],"while":[65],"others":[66],"not":[68],"(unlabeled).":[69],"Despite":[70],"large":[72],"amount":[73],"existing":[75],"work":[76],"fairness-aware":[78,118],"(semi-)supervised":[83],"unsupervised":[85],"settings,":[86],"fairness":[88,150,194],"issue":[89],"is":[90,104,179],"largely":[91],"under-explored":[92],"aforementioned":[95],"Positive":[96],"Unlabeled":[98],"Learning":[99],"(PUL)":[100],"context,":[101],"where":[102],"it":[103],"usually":[105],"more":[106],"severe.":[107],"this":[109,113],"paper,":[110],"to":[111,135,181],"alleviate":[112],"tension,":[114],"we":[115,133,163],"propose":[116],"a":[117,165],"PUL":[119,212],"method":[120],"named":[121],"FairPUL.":[122],"particular,":[124],"for":[125,161],"binary":[126],"classification":[127,190],"over":[128],"two":[131],"populations,":[132],"aim":[134],"achieve":[136],"similar":[137],"true":[138],"rates":[140,144],"false":[142],"both":[146,170,188,211],"populations":[147],"metric.":[151,195],"Based":[152],"analysis":[155],"optimal":[158],"fair":[159,214],"classifier":[160],"PUL,":[162],"design":[164],"model-agnostic":[166],"post-processing":[167],"framework,":[168],"leveraging":[169],"examples":[173],"ones.":[176],"Our":[177],"framework":[178,207],"proven":[180],"statistically":[183],"consistent":[184],"terms":[186],"error":[191],"Experiments":[196],"synthetic":[199],"sets":[203],"demonstrate":[204],"that":[205],"outperforms":[208],"state-of-the-art":[209],"classification.":[215]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
