{"id":"https://openalex.org/W3035335952","doi":"https://doi.org/10.1109/tkde.2020.3002567","title":"Fairness in Semi-Supervised Learning: Unlabeled Data Help to Reduce Discrimination","display_name":"Fairness in Semi-Supervised Learning: Unlabeled Data Help to Reduce Discrimination","publication_year":2020,"publication_date":"2020-06-15","ids":{"openalex":"https://openalex.org/W3035335952","doi":"https://doi.org/10.1109/tkde.2020.3002567","mag":"3035335952"},"language":"en","primary_location":{"id":"doi:10.1109/tkde.2020.3002567","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2020.3002567","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"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 Knowledge and Data Engineering","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2009.12040","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Tao Zhang","orcid":"https://orcid.org/0000-0003-4696-641X"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tao Zhang","raw_affiliation_strings":["Centre for Cyber Security and Privacy, School of Computer Science, University of Technology, Sydney, Ultimo, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-4696-641X","affiliations":[{"raw_affiliation_string":"Centre for Cyber Security and Privacy, School of Computer Science, University of Technology, Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tianqing Zhu","orcid":"https://orcid.org/0000-0003-3411-7947"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tianqing Zhu","raw_affiliation_strings":["Centre for Cyber Security and Privacy, School of Computer Science, University of Technology, Sydney, Ultimo, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3411-7947","affiliations":[{"raw_affiliation_string":"Centre for Cyber Security and Privacy, School of Computer Science, University of Technology, Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jing Li","orcid":"https://orcid.org/0000-0001-7584-1240"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jing Li","raw_affiliation_strings":["Centre for Artificial Intelligence, University of Technology Sydney, Ultimo, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7584-1240","affiliations":[{"raw_affiliation_string":"Centre for Artificial Intelligence, University of Technology Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Mengde Han","orcid":"https://orcid.org/0000-0003-2017-5038"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mengde Han","raw_affiliation_strings":["Centre for Cyber Security and Privacy, School of Computer Science, University of Technology, Sydney, Ultimo, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0003-2017-5038","affiliations":[{"raw_affiliation_string":"Centre for Cyber Security and Privacy, School of Computer Science, University of Technology, Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wanlei Zhou","orcid":"https://orcid.org/0000-0002-1680-2521"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Wanlei Zhou","raw_affiliation_strings":["Centre for Cyber Security and Privacy, School of Computer Science, University of Technology, Sydney, Ultimo, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0002-1680-2521","affiliations":[{"raw_affiliation_string":"Centre for Cyber Security and Privacy, School of Computer Science, University of Technology, Sydney, Ultimo, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":null,"display_name":"Philip S. Yu","orcid":"https://orcid.org/0000-0002-3491-5968"},"institutions":[{"id":"https://openalex.org/I39422238","display_name":"University of Illinois Chicago","ror":"https://ror.org/02mpq6x41","country_code":"US","type":"education","lineage":["https://openalex.org/I39422238"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip S. Yu","raw_affiliation_strings":["Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA"],"raw_orcid":"https://orcid.org/0000-0002-3491-5968","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA","institution_ids":["https://openalex.org/I39422238"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":4.8743,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.95459825,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":"34","issue":"4","first_page":"1763","last_page":"1774"},"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":0.9509999752044678,"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":0.9509999752044678,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.008200000040233135,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.004399999976158142,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/variance","display_name":"Variance (accounting)","score":0.5666999816894531},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5602999925613403},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4952000081539154},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.48890000581741333},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4438999891281128},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4334999918937683},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43309998512268066},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4032999873161316}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8266000151634216},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7135000228881836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6825000047683716},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5666999816894531},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5602999925613403},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4952000081539154},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.48890000581741333},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4438999891281128},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4334999918937683},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43309998512268066},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.39570000767707825},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.3903999924659729},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.350600004196167},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29330000281333923},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.26840001344680786}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tkde.2020.3002567","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tkde.2020.3002567","pdf_url":null,"source":{"id":"https://openalex.org/S30698027","display_name":"IEEE Transactions on Knowledge and Data Engineering","issn_l":"1041-4347","issn":["1041-4347","1558-2191","2326-3865"],"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 Knowledge and Data Engineering","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2009.12040","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.12040","pdf_url":"https://arxiv.org/pdf/2009.12040","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,"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:2009.12040","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2009.12040","pdf_url":"https://arxiv.org/pdf/2009.12040","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,"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":[],"awards":[{"id":"https://openalex.org/G2142201510","display_name":"III: Small: Exploiting the Massive User Generated Utterances for Intent Mining under Scarce Annotations","funder_award_id":"1909323","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3118254216","display_name":"III: Medium: Collaborative Research: An Extensible Heterogeneous Network Embedding Framework with Application Specific Adaptation","funder_award_id":"1763325","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G779482199","display_name":"III: Small: Fusion of Heterogeneous Networks for Synergistic Knowledge Discovery","funder_award_id":"1526499","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G979281235","display_name":"SaTC: CORE: Small: Collaborative: Learning Dynamic and Robust Defenses Against Co-Adaptive Spammers","funder_award_id":"1930941","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/F4320334704","display_name":"Australian Research Council","ror":"https://ror.org/05mmh0f86"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W1961345416","https://openalex.org/W2100960835","https://openalex.org/W2116666691","https://openalex.org/W2116984840","https://openalex.org/W2904239671","https://openalex.org/W2905029197","https://openalex.org/W2943618673","https://openalex.org/W2960028325","https://openalex.org/W2962925443","https://openalex.org/W2963116854","https://openalex.org/W2964031043","https://openalex.org/W2982397075","https://openalex.org/W3008319555","https://openalex.org/W4212883601","https://openalex.org/W4289258088","https://openalex.org/W6640850671","https://openalex.org/W6680140577","https://openalex.org/W6684072790","https://openalex.org/W6728551298","https://openalex.org/W6734300861","https://openalex.org/W6738870502","https://openalex.org/W6744110554","https://openalex.org/W6748256130","https://openalex.org/W6748377460","https://openalex.org/W6748650672","https://openalex.org/W6750028572","https://openalex.org/W6751917733","https://openalex.org/W6753999205","https://openalex.org/W6754939487","https://openalex.org/W6757410267","https://openalex.org/W6758443788","https://openalex.org/W6758760113","https://openalex.org/W6762584528","https://openalex.org/W6763290930","https://openalex.org/W6764441581","https://openalex.org/W6769615536","https://openalex.org/W6775604136","https://openalex.org/W7014198846"],"related_works":[],"abstract_inverted_index":{"A":[0,163,188],"growing":[1],"specter":[2],"in":[3,42,106,121,132,185],"the":[4,11,76,79,93,133,173,179],"rise":[5],"of":[6,30,78,101,115,128,167,176,190],"machine":[7,15],"learning":[8,16,62,131,156],"is":[9,22,109,201],"whether":[10,60],"decisions":[12],"made":[13],"by":[14],"models":[17,39,96],"are":[18,45],"fair.":[19],"While":[20],"research":[21],"already":[23],"underway":[24],"to":[25,33,58,66,84,103,139,148,157,203,207],"formalize":[26],"a":[27,85,113,126,145,209],"machine-learning":[28],"concept":[29],"fairness":[31,89,184],"and":[32,90,117,153,160,170,178,194,214],"design":[34],"frameworks":[35],"for":[36,142],"building":[37],"fair":[38,129,151],"with":[40],"sacrifice":[41],"accuracy,":[43],"most":[44,94],"geared":[46],"toward":[47],"either":[48],"supervised":[49],"or":[50],"unsupervised":[51],"learning.":[52,187],"Yet":[53],"two":[54],"observations":[55],"inspired":[56],"us":[57],"wonder":[59],"semi-supervised":[61,130,186],"might":[63],"be":[64],"useful":[65],"solve":[67],"discrimination":[68,177],"problems.":[69],"First,":[70],"previous":[71],"study":[72],"showed":[73],"that":[74,198],"increasing":[75],"size":[77],"training":[80],"set":[81,189],"may":[82],"lead":[83],"better":[86,210],"trade-off":[87,211],"between":[88,212],"accuracy.":[91],"Second,":[92],"powerful":[95],"today":[97],"require":[98],"an":[99],"enormous":[100],"data":[102,206],"train":[104],"which,":[105],"practical":[107],"terms,":[108],"likely":[110],"possible":[111],"from":[112],"combination":[114],"labeled":[116],"unlabeled":[118,143,205],"data.":[119],"Hence,":[120],"this":[122],"paper,":[123],"we":[124],"present":[125],"framework":[127],"pre-processing":[134],"phase,":[135],"including":[136],"pseudo":[137],"labeling":[138],"predict":[140],"labels":[141],"data,":[144],"re-sampling":[146],"method":[147,200],"obtain":[149],"multiple":[150],"datasets":[152,196],"lastly,":[154],"ensemble":[155],"improve":[158],"accuracy":[159,213],"decrease":[161],"discrimination.":[162,215],"theoretical":[164],"decomposition":[165],"analysis":[166],"bias,":[168],"variance":[169],"noise":[171],"highlights":[172],"different":[174],"sources":[175],"impact":[180],"they":[181],"have":[182],"on":[183,192],"experiments":[191],"real-world":[193],"synthetic":[195],"show":[197],"our":[199],"able":[202],"use":[204],"achieve":[208]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2020-06-19T00:00:00"}