{"id":"https://openalex.org/W7154492018","doi":"https://doi.org/10.48550/arxiv.2604.11934","title":"BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models","display_name":"BiasIG: Benchmarking Multi-dimensional Social Biases in Text-to-Image Models","publication_year":2026,"publication_date":"2026-04-13","ids":{"openalex":"https://openalex.org/W7154492018","doi":"https://doi.org/10.48550/arxiv.2604.11934"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.11934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11934","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.11934","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103494566","display_name":"Hanjun Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Hanjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133704099","display_name":"Zhimu Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Zhimu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133717058","display_name":"Haoyu Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Haoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133687990","display_name":"Ziye Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deng, Ziye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133641151","display_name":"Ruizhe Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Ruizhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133637614","display_name":"Xinfeng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xinfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133714887","display_name":"Zuozhu Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zuozhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133662866","display_name":"Hanan Salam","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salam, Hanan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.1670999974012375,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.1670999974012375,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.16359999775886536,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.15960000455379486,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/debiasing","display_name":"Debiasing","score":0.881600022315979},{"id":"https://openalex.org/keywords/conflation","display_name":"Conflation","score":0.7980999946594238},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6521999835968018},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.550000011920929},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5062999725341797},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.3919000029563904},{"id":"https://openalex.org/keywords/unintended-consequences","display_name":"Unintended consequences","score":0.383899986743927},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.37470000982284546}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.881600022315979},{"id":"https://openalex.org/C130440534","wikidata":"https://www.wikidata.org/wiki/Q14946528","display_name":"Conflation","level":2,"score":0.7980999946594238},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6521999835968018},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6230999827384949},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.550000011920929},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5284000039100647},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5062999725341797},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.453900009393692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4372999966144562},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3919000029563904},{"id":"https://openalex.org/C2776889888","wikidata":"https://www.wikidata.org/wiki/Q1135789","display_name":"Unintended consequences","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.37470000982284546},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.37279999256134033},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.3303000032901764},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C27415008","wikidata":"https://www.wikidata.org/wiki/Q7256382","display_name":"Psychological intervention","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C127627568","wikidata":"https://www.wikidata.org/wiki/Q1639361","display_name":"Sociotechnical system","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.2980000078678131},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.27970001101493835},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.27379998564720154},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26019999384880066},{"id":"https://openalex.org/C107062074","wikidata":"https://www.wikidata.org/wiki/Q109701697","display_name":"Prejudice (legal term)","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.11934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11934","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.11934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.11934","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6488535404205322}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Text-to-Image":[0],"(T2I)":[1],"generative":[2,41],"models":[3,115],"have":[4],"revolutionized":[5],"content":[6],"creation,":[7],"yet":[8],"they":[9],"inherently":[10],"risk":[11],"amplifying":[12],"societal":[13],"biases.":[14],"While":[15],"sociological":[16,67],"research":[17],"provides":[18],"systematic":[19],"classifications":[20],"of":[21,40,62],"bias,":[22],"existing":[23],"T2I":[24,114],"benchmarks":[25],"largely":[26],"conflate":[27],"these":[28],"nuances":[29],"or":[30],"focus":[31],"narrowly":[32],"on":[33,112,135,143],"occupational":[34],"stereotypes,":[35],"leaving":[36],"the":[37],"multi-dimensional":[38],"nature":[39],"bias":[42],"inadequately":[43],"measured.":[44],"In":[45],"this":[46],"paper,":[47],"we":[48,88],"introduce":[49],"BiasIG,":[50],"a":[51,59,90,96,125,150,163,172],"unified":[52],"benchmark":[53,187],"that":[54],"quantifies":[55],"social":[56],"biases":[57,74],"across":[58,75],"curated":[60],"dataset":[61],"47,040":[63],"prompts.":[64],"Grounded":[65],"in":[66,169,182],"and":[68,85,116,146],"machine":[69],"ethics":[70],"frameworks,":[71],"BiasIG":[72,123],"disentangles":[73],"4":[76],"dimensions":[77],"to":[78,107,167],"enable":[79],"fine-grained":[80],"diagnosis.":[81],"To":[82],"facilitate":[83],"scalable":[84],"reliable":[86],"evaluation,":[87],"propose":[89],"fully":[91],"automated":[92],"pipeline":[93],"powered":[94],"by":[95],"fine-tuned":[97],"multi-modal":[98],"large":[99],"language":[100],"model,":[101],"achieving":[102],"high":[103],"alignment":[104],"accuracy":[105],"comparable":[106],"human":[108],"experts.":[109],"Extensive":[110],"experiments":[111],"8":[113],"3":[117],"debiasing":[118,147],"methods":[119,148],"not":[120],"only":[121],"validate":[122],"as":[124,179],"robust":[126],"diagnostic":[127],"tool,":[128],"but":[129],"also":[130],"reveal":[131],"critical":[132],"insights:":[133],"interventions":[134],"protected":[136],"attributes":[137],"often":[138],"trigger":[139],"unintended":[140],"confounding":[141],"effects":[142],"unrelated":[144],"demographics,":[145],"exhibit":[149],"persistent":[151],"tendency":[152],"toward":[153],"discrimination":[154],"rather":[155],"than":[156],"mere":[157],"ignorance.":[158],"Our":[159],"work":[160],"advocates":[161],"for":[162,175],"precise,":[164],"taxonomy-driven":[165],"approach":[166],"fairness":[168],"AIGC,":[170],"providing":[171],"theoretical":[173],"framework":[174],"using":[176],"BiasIG's":[177],"metrics":[178],"feedback":[180],"signals":[181],"future":[183],"closed-loop":[184],"mitigation.":[185],"The":[186],"is":[188],"openly":[189],"available":[190],"at":[191],"https://github.com/Astarojth/BiasIG.":[192]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-16T00:00:00"}