{"id":"https://openalex.org/W4320024232","doi":"https://doi.org/10.1109/bigdata55660.2022.10020588","title":"InfoFair: Information-Theoretic Intersectional Fairness","display_name":"InfoFair: Information-Theoretic Intersectional Fairness","publication_year":2022,"publication_date":"2022-12-17","ids":{"openalex":"https://openalex.org/W4320024232","doi":"https://doi.org/10.1109/bigdata55660.2022.10020588"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata55660.2022.10020588","doi":"https://doi.org/10.1109/bigdata55660.2022.10020588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020588","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"IEEE International Conference on Big Data (BigData)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2105.11069","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100700905","display_name":"Jian Li Kang","orcid":"https://orcid.org/0000-0003-3902-7131","observed_orcids":["https://orcid.org/0000-0003-3902-7131"]},"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":"Jian Kang","raw_affiliation_strings":["University of Illinois Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004845067","display_name":"Tiankai Xie","orcid":"https://orcid.org/0009-0008-8832-0063","observed_orcids":["https://orcid.org/0009-0008-8832-0063"]},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tiankai Xie","raw_affiliation_strings":["Arizona State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008463509","display_name":"Xintao Wu","orcid":"https://orcid.org/0000-0002-2823-3063","observed_orcids":["https://orcid.org/0000-0002-2823-3063","https://orcid.org/0000-0002-6847-5673"]},"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":"Xintao Wu","raw_affiliation_strings":["University of Arkansas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Arkansas","institution_ids":["https://openalex.org/I78715868"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026799813","display_name":"Ross Maciejewski","orcid":"https://orcid.org/0000-0001-8803-6355","observed_orcids":["https://orcid.org/0000-0001-8803-6355"]},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ross Maciejewski","raw_affiliation_strings":["Arizona State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068043486","display_name":"Hanghang Tong","orcid":"https://orcid.org/0000-0003-4405-3887","observed_orcids":["https://orcid.org/0000-0003-4405-3887"]},"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":"Hanghang Tong","raw_affiliation_strings":["University of Illinois Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.2172,"has_fulltext":true,"cited_by_count":24,"citation_normalized_percentile":{"value":0.95149748,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1455","last_page":"1464"},"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":0.9854000210762024,"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":0.9854000210762024,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.0031999999191612005,"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":0.0017999999690800905,"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/algorithmic-fairness","display_name":"algorithmic fairness","score":0.8500000238418579},{"id":"https://openalex.org/keywords/intersectional-fairness","display_name":"intersectional fairness","score":0.9670000076293945},{"id":"https://openalex.org/keywords/group-fairness","display_name":"group fairness","score":0.871999979019165},{"id":"https://openalex.org/keywords/multiple-sensitive-attributes","display_name":"multiple sensitive attributes","score":0.7979999780654907},{"id":"https://openalex.org/keywords/statistical-parity","display_name":"statistical parity","score":0.8790000081062317},{"id":"https://openalex.org/keywords/mutual-information-minimization","display_name":"mutual information minimization","score":0.9959999918937683},{"id":"https://openalex.org/keywords/density-ratio-estimation","display_name":"density ratio estimation","score":0.6359999775886536},{"id":"https://openalex.org/keywords/fair-classification","display_name":"fair classification","score":0.828000009059906},{"id":"https://openalex.org/keywords/bias-mitigation","display_name":"bias mitigation","score":0.9350000023841858}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7086904048919678},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.617060661315918},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.6042346954345703},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6015825867652893},{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.42363640666007996},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39484041929244995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3737488090991974},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32352912425994873},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17764657735824585},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.10552793741226196},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"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":3,"locations":[{"id":"doi:10.1109/bigdata55660.2022.10020588","doi":"https://doi.org/10.1109/bigdata55660.2022.10020588","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata55660.2022.10020588","pdf_url":null,"source":{"id":"https://openalex.org/S4363607709","display_name":"IEEE International Conference on Big Data (BigData)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2105.11069","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.11069","pdf_url":"https://arxiv.org/pdf/2105.11069","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":"doi:10.48550/arxiv.2105.11069","doi":"https://doi.org/10.48550/arxiv.2105.11069","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2105.11069","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":"pmh:oai:arXiv.org:2105.11069","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2105.11069","pdf_url":"https://arxiv.org/pdf/2105.11069","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"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.8974000215530396}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1490481123","display_name":"FAI: Towards a Computational Foundation for Fair Network Learning","funder_award_id":"1939725","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1766801816","display_name":null,"funder_award_id":"2020-67021-32799","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1957999595","display_name":null,"funder_award_id":"#2020-67021-32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G2926720356","display_name":null,"funder_award_id":"32799","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G3748775989","display_name":null,"funder_award_id":"HR001121C0165","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4145031322","display_name":null,"funder_award_id":"17STQAC00001-03-03","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4403888875","display_name":null,"funder_award_id":"W911NF2110088","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G5620962805","display_name":null,"funder_award_id":"67021","funder_id":"https://openalex.org/F4320332299","funder_display_name":"National Institute of Food and Agriculture"},{"id":"https://openalex.org/G5838847886","display_name":"CAREER: Network Robustification: Theories, Algorithms and Applications","funder_award_id":"1947135","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7055289205","display_name":"FAI: A novel paradigm for fairness-aware deep learning models on data streams","funder_award_id":"2147375","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7091742882","display_name":null,"funder_award_id":"HR001121C0165","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G984405641","display_name":"Collaborative Research: Towards a Theoretic Foundation for Optimal Deep Graph Learning","funder_award_id":"2134079","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"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320332299","display_name":"National Institute of Food and Agriculture","ror":"https://ror.org/05qx3fv49"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4320024232.pdf","grobid_xml":"https://content.openalex.org/works/W4320024232.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W115285041","https://openalex.org/W1522301498","https://openalex.org/W1686946872","https://openalex.org/W1909320841","https://openalex.org/W1959608418","https://openalex.org/W1961345416","https://openalex.org/W1995875735","https://openalex.org/W2014352947","https://openalex.org/W2046432209","https://openalex.org/W2100960835","https://openalex.org/W2152790380","https://openalex.org/W2162670686","https://openalex.org/W2166944917","https://openalex.org/W2170612786","https://openalex.org/W2419501139","https://openalex.org/W2519887557","https://openalex.org/W2530395818","https://openalex.org/W2547875792","https://openalex.org/W2560858617","https://openalex.org/W2599025709","https://openalex.org/W2618825949","https://openalex.org/W2768894107","https://openalex.org/W2783562753","https://openalex.org/W2784397426","https://openalex.org/W2785011159","https://openalex.org/W2803832867","https://openalex.org/W2887997457","https://openalex.org/W2888487581","https://openalex.org/W2901823434","https://openalex.org/W2945903605","https://openalex.org/W2948284172","https://openalex.org/W2949200088","https://openalex.org/W2962787423","https://openalex.org/W2962925443","https://openalex.org/W2981984683","https://openalex.org/W2989096391","https://openalex.org/W3007501395","https://openalex.org/W3032340379","https://openalex.org/W3036785680","https://openalex.org/W3209185340","https://openalex.org/W4232613155","https://openalex.org/W4297808394"],"related_works":["https://openalex.org/W3080361879","https://openalex.org/W2989096391","https://openalex.org/W7159670586","https://openalex.org/W2895786087","https://openalex.org/W4221148709","https://openalex.org/W4290878049","https://openalex.org/W4366091953","https://openalex.org/W4415428513","https://openalex.org/W3011598664","https://openalex.org/W7138106448"],"abstract_inverted_index":{"Algorithmic":[0],"fairness":[1,99,107],"is":[2,17,64,109,143,179],"becoming":[3],"increasingly":[4],"important":[5],"in":[6,61,206],"data":[7],"mining":[8],"and":[9,130,160,172,191],"machine":[10],"learning.":[11],"Among":[12],"others,":[13],"a":[14,31,41,72,104,125,132,146,201],"foundational":[15],"notation":[16],"group":[18,28,106],"fairness.":[19],"The":[20,140],"vast":[21],"majority":[22],"of":[23,51,82,96,119,149,189,196],"the":[24,46,49,62,94,154,166,170,173,207,223,230],"existing":[25],"works":[26],"on":[27,36,211],"fairness,":[29,190],"with":[30,38,76,200,226],"few":[32],"exceptions,":[33],"primarily":[34],"focus":[35],"debiasing":[37],"respect":[39,77],"to":[40,78,86,137,144,181,229],"single":[42],"sensitive":[43,53,80,117,161],"attribute,":[44],"despite":[45],"fact":[47],"that":[48,69,216],"co-existence":[50],"multiple":[52,116],"attributes":[54,81,118],"(e.g.,":[55],"gender,":[56],"race,":[57],"marital":[58],"status,":[59],"etc.)":[60],"real-world":[63,213],"commonplace.":[65],"As":[66],"such,":[67],"methods":[68],"can":[70,220],"ensure":[71],"fair":[73,208],"learning":[74,158,197],"outcome":[75],"all":[79],"concern":[83],"simultaneously":[84],"need":[85],"be":[87],"developed.":[88],"In":[89],"this":[90],"paper,":[91],"we":[92],"study":[93],"problem":[95,129],"information-theoretic":[97],"intersectional":[98],"(InfoFair),":[100],"where":[101],"statistical":[102,187],"parity,":[103],"representative":[105],"measure,":[108],"guaranteed":[110],"among":[111],"demographic":[112],"groups":[113],"formed":[114],"by":[115],"interest.":[120],"We":[121],"formulate":[122],"it":[123],"as":[124,163,165],"mutual":[126,150],"information":[127],"minimization":[128],"propose":[131],"generic":[133],"end-to-end":[134],"algorithmic":[135],"framework":[136,178,219],"solve":[138],"it.":[139],"key":[141],"idea":[142],"leverage":[145],"variational":[147,155,171],"representation":[148],"information,":[151],"which":[152],"considers":[153],"distribution":[156],"between":[157,169],"outcomes":[159],"attributes,":[162],"well":[164],"density":[167],"ratio":[168],"original":[174],"distributions.":[175],"Our":[176],"proposed":[177,218],"generalizable":[180],"many":[182],"different":[183],"settings,":[184],"including":[185],"other":[186],"notions":[188],"could":[192],"handle":[193],"any":[194],"type":[195],"task":[198,210],"equipped":[199],"gradientbased":[202],"optimizer.":[203],"Empirical":[204],"evaluations":[205],"classification":[209,224,231],"three":[212],"datasets":[214],"demonstrate":[215],"our":[217],"effectively":[221],"debias":[222],"results":[225],"minimal":[227],"impact":[228],"accuracy.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":1}],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2023-02-12T00:00:00"}
