{"id":"https://openalex.org/W4290878049","doi":"https://doi.org/10.1145/3534678.3539302","title":"Fair Representation Learning: An Alternative to Mutual Information","display_name":"Fair Representation Learning: An Alternative to Mutual Information","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4290878049","doi":"https://doi.org/10.1145/3534678.3539302"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539302","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539302","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and 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/A5027554931","display_name":"Ji Liu","orcid":"https://orcid.org/0000-0002-9679-3716"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ji Liu","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009560003","display_name":"Zenan Li","orcid":"https://orcid.org/0000-0003-2432-1776"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zenan Li","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068643894","display_name":"Yuan Yao","orcid":"https://orcid.org/0000-0002-6913-6542"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Yao","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102888548","display_name":"Xu Feng","orcid":"https://orcid.org/0000-0001-8846-8051"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Xu","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041674680","display_name":"Xiaoxing Ma","orcid":"https://orcid.org/0000-0001-7970-1384"},"institutions":[{"id":"https://openalex.org/I881766915","display_name":"Nanjing University","ror":"https://ror.org/01rxvg760","country_code":"CN","type":"education","lineage":["https://openalex.org/I881766915"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxing Ma","raw_affiliation_strings":["Nanjing University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University, Nanjing, China","institution_ids":["https://openalex.org/I881766915"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016620131","display_name":"Miao Xu","orcid":"https://orcid.org/0000-0001-9409-6960"},"institutions":[{"id":"https://openalex.org/I165143802","display_name":"University of Queensland","ror":"https://ror.org/00rqy9422","country_code":"AU","type":"education","lineage":["https://openalex.org/I165143802"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Miao Xu","raw_affiliation_strings":["The University of Queensland, Brisbane, QLD, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Queensland, Brisbane, QLD, Australia","institution_ids":["https://openalex.org/I165143802"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068043486","display_name":"Hanghang Tong","orcid":"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 at Urbana-Champaign, Urbana-Champaign, IL, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5027554931"],"corresponding_institution_ids":["https://openalex.org/I881766915"],"apc_list":null,"apc_paid":null,"fwci":1.2753,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.82374101,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1088","last_page":"1097"},"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":0.9962999820709229,"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":0.9962999820709229,"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":0.9656000137329102,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9491000175476074,"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/mutual-information","display_name":"Mutual information","score":0.7999340295791626},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6673609018325806},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.65596604347229},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.619994044303894},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.5823850631713867},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5628606081008911},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4639211893081665},{"id":"https://openalex.org/keywords/kullback\u2013leibler-divergence","display_name":"Kullback\u2013Leibler divergence","score":0.4334884285926819},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4193781018257141},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.34063854813575745},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2546943128108978},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10065990686416626}],"concepts":[{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.7999340295791626},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6673609018325806},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.65596604347229},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.619994044303894},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.5823850631713867},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5628606081008911},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4639211893081665},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.4334884285926819},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4193781018257141},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.34063854813575745},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2546943128108978},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10065990686416626},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3534678.3539302","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539302","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W398859631","https://openalex.org/W569478347","https://openalex.org/W1964280018","https://openalex.org/W1993417296","https://openalex.org/W2018582985","https://openalex.org/W2044442377","https://openalex.org/W2056712138","https://openalex.org/W2082290707","https://openalex.org/W2578307220","https://openalex.org/W2593390416","https://openalex.org/W2753738274","https://openalex.org/W2963116854","https://openalex.org/W2974192836","https://openalex.org/W3174161356","https://openalex.org/W3181414820","https://openalex.org/W3195765557","https://openalex.org/W3215441929","https://openalex.org/W4214752017","https://openalex.org/W4289258088","https://openalex.org/W6688325169"],"related_works":["https://openalex.org/W3107178186","https://openalex.org/W2773208253","https://openalex.org/W2466816617","https://openalex.org/W3181754520","https://openalex.org/W2560646951","https://openalex.org/W2074589917","https://openalex.org/W4225940264","https://openalex.org/W4383822431","https://openalex.org/W4367047287","https://openalex.org/W2008666878"],"abstract_inverted_index":{"Learning":[0],"fair":[1,95,141,151],"representations":[2,22],"is":[3,61],"an":[4],"essential":[5],"task":[6],"to":[7,23,42,118,139],"reduce":[8],"bias":[9],"in":[10],"data-oriented":[11],"decision":[12],"making.":[13],"It":[14],"protects":[15],"minority":[16],"subgroups":[17],"by":[18],"requiring":[19],"the":[20,32,36,43,46,76,79,112,125,130],"learned":[21,53,80],"be":[24,116],"independent":[25],"of":[26,35,45,58,78],"sensitive":[27,50,101],"attributes.":[28],"To":[29],"achieve":[30],"independence,":[31],"vast":[33],"majority":[34],"existing":[37,148],"work":[38],"primarily":[39],"relaxes":[40],"it":[41],"minimization":[44],"mutual":[47,59],"information":[48,60],"between":[49],"attributes":[51,102],"and":[52,64,106],"representations.":[54,81,142],"However,":[55],"direct":[56],"computation":[57],"computationally":[62],"intractable,":[63],"various":[65],"upper":[66],"bounds":[67],"currently":[68],"used":[69],"either":[70],"are":[71,109],"still":[72],"intractable":[73],"or":[74],"contradict":[75],"utility":[77,126],"In":[82],"this":[83],"paper,":[84],"we":[85,133],"introduce":[86],"distance":[87,113],"covariance":[88,114],"as":[89],"a":[90,119,136],"new":[91],"dependence":[92],"measure":[93],"into":[94],"representation":[96,152],"learning.":[97,153],"By":[98],"observing":[99],"that":[100,145],"(e.g.,":[103],"gender,":[104],"race,":[105],"age":[107],"group)":[108],"typically":[110],"categorical,":[111],"can":[115],"converted":[117],"tractable":[120,131],"penalty":[121],"term":[122],"without":[123],"contradicting":[124],"desideratum.":[127],"Based":[128],"on":[129],"penalty,":[132],"propose":[134],"FairDisCo,":[135],"variational":[137],"method":[138],"learn":[140],"Experiments":[143],"demonstrate":[144],"FairDisCo":[146],"outperforms":[147],"competitors":[149],"for":[150]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":4}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
