{"id":"https://openalex.org/W4382677721","doi":"https://doi.org/10.1109/tpami.2023.3290949","title":"Bipartite Ranking Fairness Through a Model Agnostic Ordering Adjustment","display_name":"Bipartite Ranking Fairness Through a Model Agnostic Ordering Adjustment","publication_year":2023,"publication_date":"2023-06-30","ids":{"openalex":"https://openalex.org/W4382677721","doi":"https://doi.org/10.1109/tpami.2023.3290949","pmid":"https://pubmed.ncbi.nlm.nih.gov/37819812"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2023.3290949","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2023.3290949","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":["crossref","pubmed"],"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/A5069563876","display_name":"Sen Cui","orcid":"https://orcid.org/0000-0003-1224-5569"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Cui","raw_affiliation_strings":["Department of Automation, BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1224-5569","affiliations":[{"raw_affiliation_string":"Department of Automation, BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068293793","display_name":"Weishen Pan","orcid":"https://orcid.org/0000-0002-3274-5037"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weishen Pan","raw_affiliation_strings":["Department of Automation, BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3274-5037","affiliations":[{"raw_affiliation_string":"Department of Automation, BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065063835","display_name":"Changshui Zhang","orcid":"https://orcid.org/0000-0002-8088-367X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changshui Zhang","raw_affiliation_strings":["Department of Automation, BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8088-367X","affiliations":[{"raw_affiliation_string":"Department of Automation, BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455768","display_name":"Fei Wang","orcid":"https://orcid.org/0000-0001-9459-9461"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fei Wang","raw_affiliation_strings":["Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, Ithaca, NY, USA","Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, USA"],"raw_orcid":"https://orcid.org/0000-0001-9459-9461","affiliations":[{"raw_affiliation_string":"Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]},{"raw_affiliation_string":"Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9579,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.88820643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"45","issue":"11","first_page":"13235","last_page":"13249"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8680999875068665,"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"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8680999875068665,"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/T10804","display_name":"Health Systems, Economic Evaluations, Quality of Life","score":0.8618000149726868,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.8407999873161316,"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/ranking","display_name":"Ranking (information retrieval)","score":0.7573354244232178},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7149340510368347},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6615936756134033},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5587307214736938},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5165016055107117},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.4864470660686493},{"id":"https://openalex.org/keywords/ranking-svm","display_name":"Ranking SVM","score":0.46300366520881653},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.426270991563797},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2349839210510254}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7573354244232178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7149340510368347},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6615936756134033},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5587307214736938},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5165016055107117},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.4864470660686493},{"id":"https://openalex.org/C124975894","wikidata":"https://www.wikidata.org/wiki/Q7293290","display_name":"Ranking SVM","level":3,"score":0.46300366520881653},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.426270991563797},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2349839210510254},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2023.3290949","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2023.3290949","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:37819812","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37819812","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}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2275947126","display_name":null,"funder_award_id":"2020AAA0107800","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":83,"referenced_works":["https://openalex.org/W1517796226","https://openalex.org/W1552767446","https://openalex.org/W1559060276","https://openalex.org/W1956343362","https://openalex.org/W1987463134","https://openalex.org/W2004672916","https://openalex.org/W2014352947","https://openalex.org/W2040825624","https://openalex.org/W2072055539","https://openalex.org/W2097246321","https://openalex.org/W2100960835","https://openalex.org/W2116666691","https://openalex.org/W2116984840","https://openalex.org/W2124168655","https://openalex.org/W2140396673","https://openalex.org/W2149427297","https://openalex.org/W2153578526","https://openalex.org/W2153863985","https://openalex.org/W2157825442","https://openalex.org/W2157928966","https://openalex.org/W2162670686","https://openalex.org/W2194775991","https://openalex.org/W2396881363","https://openalex.org/W2530395818","https://openalex.org/W2544318541","https://openalex.org/W2558430786","https://openalex.org/W2704480242","https://openalex.org/W2725155646","https://openalex.org/W2778670458","https://openalex.org/W2787991113","https://openalex.org/W2791170418","https://openalex.org/W2803923024","https://openalex.org/W2891400669","https://openalex.org/W2911448806","https://openalex.org/W2913539872","https://openalex.org/W2922169395","https://openalex.org/W2949200088","https://openalex.org/W2950173087","https://openalex.org/W2962790618","https://openalex.org/W2963116854","https://openalex.org/W2963189767","https://openalex.org/W2963223295","https://openalex.org/W2963917042","https://openalex.org/W2964023221","https://openalex.org/W2964031043","https://openalex.org/W2965119030","https://openalex.org/W2995843219","https://openalex.org/W2997398218","https://openalex.org/W3007303805","https://openalex.org/W3035335952","https://openalex.org/W3100906513","https://openalex.org/W3101973032","https://openalex.org/W3102092462","https://openalex.org/W3102518922","https://openalex.org/W3103891807","https://openalex.org/W3171765473","https://openalex.org/W4289258088","https://openalex.org/W4294241863","https://openalex.org/W4385245566","https://openalex.org/W4386564359","https://openalex.org/W6632992187","https://openalex.org/W6633301734","https://openalex.org/W6640850671","https://openalex.org/W6682492585","https://openalex.org/W6684072790","https://openalex.org/W6712503630","https://openalex.org/W6728551298","https://openalex.org/W6729907257","https://openalex.org/W6737016370","https://openalex.org/W6739901393","https://openalex.org/W6740303850","https://openalex.org/W6747390009","https://openalex.org/W6748256130","https://openalex.org/W6750028572","https://openalex.org/W6751691900","https://openalex.org/W6755072255","https://openalex.org/W6758266414","https://openalex.org/W6760021297","https://openalex.org/W6763290930","https://openalex.org/W6765858885","https://openalex.org/W6766396606","https://openalex.org/W6774322652","https://openalex.org/W6785528530"],"related_works":["https://openalex.org/W2315491162","https://openalex.org/W2562198007","https://openalex.org/W2368840343","https://openalex.org/W4297816538","https://openalex.org/W2031468273","https://openalex.org/W2370100764","https://openalex.org/W2187479119","https://openalex.org/W2073542340","https://openalex.org/W4307011114","https://openalex.org/W3127142483"],"abstract_inverted_index":{"Recently,":[0],"with":[1,146,209,230,245],"the":[2,32,37,42,48,69,111,123,198,213,216,222],"applications":[3],"of":[4,20,122,207,215,226],"algorithms":[5],"in":[6,22,67,106],"various":[7,147],"risky":[8],"scenarios,":[9],"algorithmic":[10],"fairness":[11,92,105,152,158,203],"has":[12],"been":[13],"a":[14,53,89,97,119,139,194,205,240,249],"serious":[15],"concern":[16],"and":[17,47,93,109,135,150,156,183,201,248,254],"received":[18],"lots":[19],"interest":[21],"machine":[23],"learning":[24],"community.":[25],"In":[26,115,160],"this":[27],"article,":[28],"we":[29,95,117],"focus":[30],"on":[31,178,204],"bipartite":[33,107],"ranking":[34,54,71,108,151,202,218,256],"scenario,":[35],"where":[36],"instances":[38,59],"come":[39],"from":[40],"either":[41],"positive":[43,58],"or":[44],"negative":[45,62],"class":[46],"goal":[49],"is":[50,144],"to":[51,162,169],"learn":[52],"function":[55,72],"that":[56,237],"ranks":[57],"higher":[60],"than":[61],"ones.":[63],"We":[64,173],"are":[65],"interested":[66],"whether":[68],"learned":[70],"can":[73,166],"cause":[74],"systematic":[75],"disparity":[76],"across":[77,131],"different":[78,132,210,227],"protected":[79,133,171],"groups":[80,134,228],"defined":[81],"by":[82],"sensitive":[83],"attributes.":[84],"While":[85],"there":[86],"could":[87],"be":[88,167],"trade-off":[90],"between":[91,197,252],"performance,":[94],"propose":[96],"model":[98],"agnostic":[99],"post-processing":[100],"framework":[101],"xOrder":[102,143,165,191,220,238],"for":[103],"achieving":[104],"maintaining":[110],"algorithm":[112,177,199],"classification":[113,148],"performance.":[114],"particular,":[116],"optimize":[118],"weighted":[120],"sum":[121],"utility":[124,200],"as":[125],"identifying":[126],"an":[127],"optimal":[128],"warping":[129],"path":[130],"solve":[136],"it":[137],"through":[138],"dynamic":[140],"programming":[141],"process.":[142],"compatible":[145],"models":[149],"metrics,":[153],"including":[154],"supervised":[155],"unsupervised":[157],"metrics.":[159,211],"addition":[161],"binary":[163],"groups,":[164],"applied":[168],"multiple":[170],"groups.":[172],"evaluate":[174],"our":[175],"proposed":[176],"four":[179],"benchmark":[180],"data":[181],"sets":[182],"two":[184],"real-world":[185],"patient":[186],"electronic":[187],"health":[188],"record":[189],"repositories.":[190],"consistently":[192],"achieves":[193,239],"better":[195],"balance":[196],"variety":[206],"datasets":[208],"From":[212],"visualization":[214],"calibrated":[217],"scores,":[219],"mitigates":[221],"score":[223,257],"distribution":[224],"shifts":[225],"compared":[229],"baselines.":[231],"Moreover,":[232],"additional":[233],"analytical":[234],"results":[235],"verify":[236],"robust":[241],"performance":[242],"when":[243],"faced":[244],"fewer":[246],"samples":[247],"bigger":[250],"difference":[251],"training":[253],"testing":[255],"distributions.":[258]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
