{"id":"https://openalex.org/W4414360582","doi":"https://doi.org/10.24963/ijcai.2025/50","title":"FADE: Towards Fairness-aware Data Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models","display_name":"FADE: Towards Fairness-aware Data Generation for Domain Generalization via Classifier-Guided Score-based Diffusion Models","publication_year":2025,"publication_date":"2025-09-01","ids":{"openalex":"https://openalex.org/W4414360582","doi":"https://doi.org/10.24963/ijcai.2025/50"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2025/50","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/50","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/A5040926650","display_name":"Yujie Lin","orcid":"https://orcid.org/0000-0002-0514-6186"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]},{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yujie Lin","raw_affiliation_strings":["Tianjin University","Xiamen University"],"affiliations":[{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"Xiamen University","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100659915","display_name":"Dong Li","orcid":"https://orcid.org/0000-0002-7363-0295"},"institutions":[{"id":"https://openalex.org/I157394403","display_name":"Baylor University","ror":"https://ror.org/005781934","country_code":"US","type":"education","lineage":["https://openalex.org/I157394403"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Li","raw_affiliation_strings":["Baylor University"],"affiliations":[{"raw_affiliation_string":"Baylor University","institution_ids":["https://openalex.org/I157394403"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107863196","display_name":"M. Shao","orcid":"https://orcid.org/0000-0002-6437-2060"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minglai Shao","raw_affiliation_strings":["Tianjin University"],"affiliations":[{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071349804","display_name":"Guihong Wan","orcid":"https://orcid.org/0000-0003-1100-4018"},"institutions":[{"id":"https://openalex.org/I2801851002","display_name":"Harvard University Press","ror":"https://ror.org/006v7bf86","country_code":"US","type":"other","lineage":["https://openalex.org/I136199984","https://openalex.org/I2801851002"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guihong Wan","raw_affiliation_strings":["Harvard University"],"affiliations":[{"raw_affiliation_string":"Harvard University","institution_ids":["https://openalex.org/I2801851002"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100767050","display_name":"Chen Zhao","orcid":"https://orcid.org/0000-0002-6400-0048"},"institutions":[{"id":"https://openalex.org/I157394403","display_name":"Baylor University","ror":"https://ror.org/005781934","country_code":"US","type":"education","lineage":["https://openalex.org/I157394403"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Zhao","raw_affiliation_strings":["Baylor University"],"affiliations":[{"raw_affiliation_string":"Baylor University","institution_ids":["https://openalex.org/I157394403"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5040926650"],"corresponding_institution_ids":["https://openalex.org/I162868743","https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14009315,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"439","last_page":"447"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.8920999765396118,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.8920999765396118,"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":0.890500009059906,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.8833000063896179,"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/generalization","display_name":"Generalization","score":0.8974999785423279},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6783999800682068},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.4309000074863434},{"id":"https://openalex.org/keywords/trustworthiness","display_name":"Trustworthiness","score":0.4271000027656555},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.38909998536109924},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.37059998512268066}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.8974999785423279},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7315000295639038},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6783999800682068},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5049999952316284},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.4309000074863434},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.4271000027656555},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41620001196861267},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4124000072479248},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37700000405311584},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2727000117301941}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2025/50","is_oa":false,"landing_page_url":"https://doi.org/10.24963/ijcai.2025/50","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":{"Fairness-aware":[0,61],"domain":[1,29],"generalization":[2,30,95],"(FairDG)":[3],"has":[4,42],"emerged":[5],"as":[6,67],"a":[7,68,81],"critical":[8],"challenge":[9],"for":[10,23,37],"deploying":[11],"trustworthy":[12],"AI":[13],"systems,":[14],"particularly":[15],"in":[16,28,153,164],"scenarios":[17],"involving":[18],"distribution":[19,38,157],"shifts.":[20,39,158],"Traditional":[21],"methods":[22,163],"addressing":[24],"fairness":[25,148],"have":[26],"failed":[27],"due":[31],"to":[32,45,71,89,109,125],"their":[33],"lack":[34],"of":[35,156],"consideration":[36],"Although":[40],"disentanglement":[41],"been":[43],"used":[44,124],"tackle":[46],"FairDG,":[47],"it":[48],"is":[49,123],"limited":[50],"by":[51],"its":[52],"strong":[53,94],"assumptions.":[54],"To":[55],"overcome":[56],"these":[57,106],"limitations,":[58],"we":[59,78,101],"propose":[60],"Classifier-Guided":[62],"Score-based":[63],"Diffusion":[64],"Models":[65],"(FADE)":[66],"novel":[69],"approach":[70],"effectively":[72,110],"address":[73],"the":[74,91,103,115,119,154,166],"FairDG":[75],"issue.":[76],"Specifically,":[77],"first":[79],"pre-train":[80],"score-based":[82],"diffusion":[83],"model":[84,92],"(SDM)":[85],"and":[86],"two":[87],"classifiers":[88,108],"equip":[90],"with":[93],"capabilities":[96],"across":[97],"different":[98],"domains.":[99],"Then,":[100],"guide":[102],"SDM":[104],"using":[105],"pre-trained":[107],"eliminate":[111],"sensitive":[112],"information":[113],"from":[114],"generated":[116,120],"data.":[117],"Finally,":[118],"fair":[121],"data":[122,134],"train":[126],"downstream":[127],"classifiers,":[128],"ensuring":[129],"robust":[130],"performance":[131],"under":[132],"new":[133],"distributions.":[135],"Extensive":[136],"experiments":[137],"on":[138],"three":[139],"real-world":[140],"datasets":[141],"demonstrate":[142],"that":[143],"FADE":[144,160],"not":[145],"only":[146],"enhances":[147],"but":[149],"also":[150],"improves":[151],"accuracy":[152],"presence":[155],"Additionally,":[159],"outperforms":[161],"existing":[162],"achieving":[165],"best":[167],"accuracy-fairness":[168],"trade-offs.":[169]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}