{"id":"https://openalex.org/W3196906567","doi":"https://doi.org/10.1109/tpami.2022.3148905","title":"Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Tasks","display_name":"Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Tasks","publication_year":2022,"publication_date":"2022-02-07","ids":{"openalex":"https://openalex.org/W3196906567","doi":"https://doi.org/10.1109/tpami.2022.3148905","mag":"3196906567","pmid":"https://pubmed.ncbi.nlm.nih.gov/35130150"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2022.3148905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3148905","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2109.00545","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102027520","display_name":"Xudong Shen","orcid":"https://orcid.org/0000-0001-9549-0986"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Xudong Shen","raw_affiliation_strings":["Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-9549-0986","affiliations":[{"raw_affiliation_string":"Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020006712","display_name":"Yongkang Wong","orcid":"https://orcid.org/0000-0002-1239-4428"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yongkang Wong","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-1239-4428","affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016415049","display_name":"Mohan Kankanhalli","orcid":"https://orcid.org/0000-0002-4846-2015"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Mohan Kankanhalli","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-4846-2015","affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102027520"],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":3.6385,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.93160967,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"45","issue":"1","first_page":"525","last_page":"538"},"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":0.9991000294685364,"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":0.9991000294685364,"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":0.9904999732971191,"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":0.9775000214576721,"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/discriminative-model","display_name":"Discriminative model","score":0.7570794820785522},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6621432900428772},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6267249584197998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5038973689079285},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.44235560297966003},{"id":"https://openalex.org/keywords/impossibility","display_name":"Impossibility","score":0.43753528594970703},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3843934237957001},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3693695068359375}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7570794820785522},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6621432900428772},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6267249584197998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5038973689079285},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.44235560297966003},{"id":"https://openalex.org/C2776261394","wikidata":"https://www.wikidata.org/wiki/Q315562","display_name":"Impossibility","level":2,"score":0.43753528594970703},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3843934237957001},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3693695068359375},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tpami.2022.3148905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3148905","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:35130150","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35130150","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null},{"id":"pmh:oai:arXiv.org:2109.00545","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.00545","pdf_url":"https://arxiv.org/pdf/2109.00545","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:2109.00545","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.00545","pdf_url":"https://arxiv.org/pdf/2109.00545","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":[{"id":"https://metadata.un.org/sdg/10","score":0.7099999785423279,"display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320709","display_name":"National Research Foundation Singapore","ror":"https://ror.org/03cpyc314"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":101,"referenced_works":["https://openalex.org/W1502922572","https://openalex.org/W1522301498","https://openalex.org/W1559060276","https://openalex.org/W1596877211","https://openalex.org/W1834627138","https://openalex.org/W1956343362","https://openalex.org/W1959608418","https://openalex.org/W2000992741","https://openalex.org/W2002870907","https://openalex.org/W2014352947","https://openalex.org/W2085427378","https://openalex.org/W2097246321","https://openalex.org/W2106887613","https://openalex.org/W2127470768","https://openalex.org/W2148825261","https://openalex.org/W2153803020","https://openalex.org/W2162670686","https://openalex.org/W2194775991","https://openalex.org/W2328111639","https://openalex.org/W2530395818","https://openalex.org/W2725155646","https://openalex.org/W2740751204","https://openalex.org/W2762339466","https://openalex.org/W2788481061","https://openalex.org/W2790025105","https://openalex.org/W2808105152","https://openalex.org/W2885549115","https://openalex.org/W2889624842","https://openalex.org/W2893425640","https://openalex.org/W2899477140","https://openalex.org/W2916903408","https://openalex.org/W2937229771","https://openalex.org/W2950018712","https://openalex.org/W2950029751","https://openalex.org/W2950095160","https://openalex.org/W2951362303","https://openalex.org/W2952676795","https://openalex.org/W2962790618","https://openalex.org/W2963053914","https://openalex.org/W2963078909","https://openalex.org/W2963446520","https://openalex.org/W2963466847","https://openalex.org/W2963839617","https://openalex.org/W2963979376","https://openalex.org/W2964031043","https://openalex.org/W2964340499","https://openalex.org/W2969985801","https://openalex.org/W2980523475","https://openalex.org/W2981869278","https://openalex.org/W2990376402","https://openalex.org/W3012964398","https://openalex.org/W3021074122","https://openalex.org/W3046748628","https://openalex.org/W3048511172","https://openalex.org/W3086067533","https://openalex.org/W3105580455","https://openalex.org/W3105928338","https://openalex.org/W3110619060","https://openalex.org/W3120485916","https://openalex.org/W3168398407","https://openalex.org/W3183009143","https://openalex.org/W4236373558","https://openalex.org/W4250730993","https://openalex.org/W4254510512","https://openalex.org/W4288088856","https://openalex.org/W4288091621","https://openalex.org/W4288091850","https://openalex.org/W4288319633","https://openalex.org/W4301216918","https://openalex.org/W6629804754","https://openalex.org/W6631190155","https://openalex.org/W6633301734","https://openalex.org/W6640850671","https://openalex.org/W6640963894","https://openalex.org/W6681723013","https://openalex.org/W6684072790","https://openalex.org/W6688325169","https://openalex.org/W6691148622","https://openalex.org/W6721933647","https://openalex.org/W6728551298","https://openalex.org/W6738077463","https://openalex.org/W6740303850","https://openalex.org/W6744261186","https://openalex.org/W6748039686","https://openalex.org/W6748256130","https://openalex.org/W6748382702","https://openalex.org/W6755490972","https://openalex.org/W6757410267","https://openalex.org/W6759251887","https://openalex.org/W6762534589","https://openalex.org/W6763012920","https://openalex.org/W6764294493","https://openalex.org/W6769457035","https://openalex.org/W6769911694","https://openalex.org/W6772979743","https://openalex.org/W6775236419","https://openalex.org/W6779698503","https://openalex.org/W6783342598","https://openalex.org/W6785891013","https://openalex.org/W6798472455","https://openalex.org/W6809680140"],"related_works":["https://openalex.org/W2384156839","https://openalex.org/W4321602641","https://openalex.org/W2394251275","https://openalex.org/W4241392912","https://openalex.org/W2596801716","https://openalex.org/W4387391601","https://openalex.org/W2141614742","https://openalex.org/W2369846953","https://openalex.org/W2362926696","https://openalex.org/W915072206"],"abstract_inverted_index":{"Motivated":[0,125],"by":[1,97,126],"scenarios":[2],"where":[3],"data":[4],"is":[5,86],"used":[6,18],"for":[7,22,26,67,75,82],"diverse":[8],"prediction":[9,69],"tasks,":[10,70],"we":[11,53,130,169],"study":[12],"whether":[13],"fair":[14,61,112,135,151],"representation":[15,62,85,115,176],"can":[16],"be":[17],"to":[19,132],"guarantee":[20,65,73],"fairness":[21,28,34,50,66,74,92,98,110,181],"unknown":[23],"tasks":[24,81],"and":[25,43,99,113,136,145,157],"multiple":[27],"notions.":[29,124],"We":[30,57],"consider":[31],"seven":[32,90],"group":[33,91],"notions":[35,93],"that":[36,120,161,168],"cover":[37],"the":[38,46,49,84,102,117,163,175],"concepts":[39],"of":[40,48,79,101,172],"independence,":[41],"separation,":[42],"calibration.":[44],"Against":[45],"backdrop":[47],"impossibility":[51],"results,":[52],"explore":[54],"approximate":[55],"fairness.":[56],"prove":[58],"that,":[59],"although":[60],"might":[63],"not":[64],"all":[68,89,123,189],"it":[71],"does":[72],"an":[76,105],"important":[77],"subset":[78],"tasks-the":[80],"which":[83,142],"discriminative.":[87],"Specifically,":[88],"are":[94,170,188],"linearly":[95],"controlled":[96],"discriminativeness":[100],"representation.":[103],"When":[104],"incompatibility":[106],"exists":[107],"between":[108],"different":[109],"notions,":[111],"discriminative":[114,137],"hits":[116],"sweet":[118],"spot":[119],"approximately":[121],"satisfies":[122],"our":[127,185],"theoretical":[128,186],"findings,":[129],"propose":[131],"learn":[133],"both":[134],"representations":[138],"using":[139,162],"pretext":[140],"loss":[141],"self-supervises":[143],"learning,":[144],"Maximum":[146],"Mean":[147],"Discrepancy":[148],"as":[149],"a":[150],"regularizer.":[152],"Experiments":[153],"on":[154],"tabular,":[155],"image,":[156],"face":[158],"datasets":[159],"show":[160],"learned":[164],"representation,":[165],"downstream":[166],"predictions":[167],"unaware":[171],"when":[173],"learning":[174],"indeed":[177],"become":[178],"fairer.":[179],"The":[180],"guarantees":[182],"computed":[183],"from":[184],"results":[187],"valid.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-07T13:39:58.223016","created_date":"2025-10-10T00:00:00"}
