{"id":"https://openalex.org/W4414360696","doi":"https://doi.org/10.24963/ijcai.2025/69","title":"FairSMOE: Mitigating Multi-Attribute Fairness Problem with Sparse Mixture-of-Experts","display_name":"FairSMOE: Mitigating Multi-Attribute Fairness Problem with Sparse Mixture-of-Experts","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360696","doi":"https://doi.org/10.24963/ijcai.2025/69"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/69","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/69","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","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/A5033198334","display_name":"Changdi Yang","orcid":"https://orcid.org/0000-0002-8848-3806"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":true,"raw_author_name":"Changdi Yang","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001806616","display_name":"Zhan Zheng","orcid":"https://orcid.org/0000-0002-5650-2099"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]},{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB","MX"],"is_corresponding":false,"raw_author_name":"Zheng Zhan","raw_affiliation_strings":["Microsoft Research","Northeastern University"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]},{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119679477","display_name":"Ci Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ci Zhang","raw_affiliation_strings":["University of Georgia"],"affiliations":[{"raw_affiliation_string":"University of Georgia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101928537","display_name":"Yifan Gong","orcid":"https://orcid.org/0000-0002-3912-097X"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Yifan Gong","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113251066","display_name":"Yize Li","orcid":null},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Yize Li","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100586102","display_name":"Zichong Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Zichong Meng","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028810874","display_name":"Jun\u2010Min Liu","orcid":"https://orcid.org/0000-0002-9876-0715"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Jun Liu","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101655527","display_name":"Xuan Shen","orcid":"https://orcid.org/0000-0003-4965-7321"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Xuan Shen","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010391473","display_name":"Hao Tang","orcid":"https://orcid.org/0000-0001-5401-7295"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Tang","raw_affiliation_strings":["Peking University"],"affiliations":[{"raw_affiliation_string":"Peking University","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100957385","display_name":"Geng Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geng Yuan","raw_affiliation_strings":["University of Georgia"],"affiliations":[{"raw_affiliation_string":"University of Georgia","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038727182","display_name":"Pu Zhao","orcid":"https://orcid.org/0000-0002-0297-789X"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Pu Zhao","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043582832","display_name":"Xue Lin","orcid":"https://orcid.org/0000-0001-6210-8883"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Xue Lin","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100651384","display_name":"Yanzhi Wang","orcid":"https://orcid.org/0000-0002-3024-7990"},"institutions":[{"id":"https://openalex.org/I87182695","display_name":"Universidad del Noreste","ror":"https://ror.org/02ahky613","country_code":"MX","type":"education","lineage":["https://openalex.org/I87182695"]}],"countries":["MX"],"is_corresponding":false,"raw_author_name":"Yanzhi Wang","raw_affiliation_strings":["Northeastern University"],"affiliations":[{"raw_affiliation_string":"Northeastern University","institution_ids":["https://openalex.org/I87182695"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":13,"corresponding_author_ids":["https://openalex.org/A5033198334"],"corresponding_institution_ids":["https://openalex.org/I87182695"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.26215199,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"610","last_page":"618"},"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":0.9695000052452087,"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":0.9695000052452087,"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":0.9164999723434448,"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/fairness-measure","display_name":"Fairness measure","score":0.6018000245094299},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5735999941825867},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.36959999799728394},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.3596000075340271},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3061000108718872},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2574000060558319}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7815999984741211},{"id":"https://openalex.org/C11867375","wikidata":"https://www.wikidata.org/wiki/Q5430671","display_name":"Fairness measure","level":4,"score":0.6018000245094299},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5735999941825867},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45019999146461487},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3849000036716461},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.36959999799728394},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3061000108718872},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2976999878883362},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.28949999809265137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26109999418258667},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C177972170","wikidata":"https://www.wikidata.org/wiki/Q17097315","display_name":"Max-min fairness","level":3,"score":0.25279998779296875},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.25200000405311584},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/69","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/69","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real\u2010world":[0],"datasets":[1],"usually":[2],"contain":[3],"multiple":[4,53],"attributes,":[5],"making":[6],"it":[7,113],"essential":[8],"to":[9,39,51,65,85,122],"ensure":[10],"fairness":[11,41,76,105,145,155],"across":[12,89],"all":[13,43,90],"of":[14,188],"them":[15],"simultaneously.":[16,92],"However,":[17,117],"different":[18],"attributes":[19,91],"may":[20],"vary":[21],"in":[22],"difficulty,":[23],"and":[24,82,98,111,151,171,177,190,198],"no":[25],"existing":[26],"approaches":[27],"have":[28],"effectively":[29],"addressed":[30],"this":[31,71],"issue.":[32],"Consequently,":[33],"an":[34,79,132],"attribute\u2010adaptive":[35],"strategy":[36],"is":[37],"needed":[38,68],"achieve":[40,86,169],"for":[42,136],"attributes.":[44],"Multi\u2010task":[45],"Learning":[46],"(MTL)":[47],"leverages":[48],"shared":[49],"information":[50],"optimize":[52],"tasks":[54],"concurrently,":[55],"while":[56],"Sparsely\u2010Gated":[57],"Mixture\u2010of\u2010Experts":[58],"(SMoE)":[59],"can":[60],"dynamically":[61],"allocate":[62],"computational":[63],"resources":[64],"the":[66,96,100,148,160,166,175],"most":[67],"tasks.":[69],"In":[70],"work,":[72],"we":[73,168],"formulate":[74],"multi\u2010attribute":[75],"issue":[77],"as":[78,165],"MTL":[80,109],"problem":[81,106,110,124],"employ":[83],"SMoE":[84,119,134],"desirable":[87],"performance":[88],"We":[93,129],"first":[94],"analyze":[95],"feasibility":[97],"find":[99],"potentiality":[101],"by":[102,114,146],"formalizing":[103],"multi-attribute":[104,137,144],"into":[107],"a":[108,163],"mitigating":[112],"using":[115],"SMoE.":[116],"vanilla":[118],"could":[120],"lead":[121],"over-utilization":[123],"which":[125,141],"causes":[126],"sub-optimal":[127],"performance.":[128],"then":[130],"proposed":[131],"innovative":[133],"framework":[135],"fair":[138],"image":[139],"classification,":[140],"further":[142],"improves":[143],"redesigning":[147],"MoE":[149],"layer":[150],"routing":[152],"policy":[153],"with":[154,181],"consideration.":[156],"Extensive":[157],"experiments":[158],"demonstrated":[159],"effectiveness.":[161],"Taking":[162],"DeiT-Small":[164],"backbone,":[167],"77.25%":[170],"86.01%":[172],"accuracy":[173],"on":[174],"ISIC2019":[176],"CelebA":[178],"dataset":[179],"respectively":[180],"Multi-attribute":[182],"Predictive":[183],"Quality":[184],"Disparity":[185],"(PQD)":[186],"score":[187],"0.801":[189],"0.787,":[191],"beating":[192],"current":[193],"state-of-the-art":[194],"methods":[195],"Muffin,":[196],"InfoFair":[197],"MultiFair.":[199]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
