{"id":"https://openalex.org/W7129078169","doi":"https://doi.org/10.48550/arxiv.2602.12659","title":"IndicFairFace: Balanced Indian Face Dataset for Auditing and Mitigating Geographical Bias in Vision-Language Models","display_name":"IndicFairFace: Balanced Indian Face Dataset for Auditing and Mitigating Geographical Bias in Vision-Language Models","publication_year":2026,"publication_date":"2026-02-13","ids":{"openalex":"https://openalex.org/W7129078169","doi":"https://doi.org/10.48550/arxiv.2602.12659"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.12659","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117249836","display_name":"Aarish Shah Mohsin","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mohsin, Aarish Shah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126162533","display_name":"Mohammed Tayyab Ilyas Khan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khan, Mohammed Tayyab Ilyas","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048625274","display_name":"Mohammad Nadeem","orcid":"https://orcid.org/0000-0003-3664-5014"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nadeem, Mohammad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048270460","display_name":"Shahab Saquib Sohail","orcid":"https://orcid.org/0000-0002-5944-7371"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sohail, Shahab Saquib","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126172709","display_name":"Erik Cambria","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cambria, Erik","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126108678","display_name":"Jiechao Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Jiechao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5117249836"],"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.6779000163078308,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.6779000163078308,"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/T11448","display_name":"Face recognition and analysis","score":0.09539999812841415,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.08320000022649765,"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/debiasing","display_name":"Debiasing","score":0.9160000085830688},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6011999845504761},{"id":"https://openalex.org/keywords/audit","display_name":"Audit","score":0.5699999928474426},{"id":"https://openalex.org/keywords/diversity","display_name":"Diversity (politics)","score":0.5641999840736389},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5468000173568726},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5256999731063843},{"id":"https://openalex.org/keywords/european-union","display_name":"European union","score":0.49810001254081726},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.44519999623298645}],"concepts":[{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.9160000085830688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6410999894142151},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6011999845504761},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.5699999928474426},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.5641999840736389},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5468000173568726},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5256999731063843},{"id":"https://openalex.org/C2910001868","wikidata":"https://www.wikidata.org/wiki/Q458","display_name":"European union","level":2,"score":0.49810001254081726},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4065000116825104},{"id":"https://openalex.org/C52130261","wikidata":"https://www.wikidata.org/wiki/Q39825","display_name":"Census","level":3,"score":0.4007999897003174},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3953999876976013},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3675999939441681},{"id":"https://openalex.org/C45555294","wikidata":"https://www.wikidata.org/wiki/Q28113351","display_name":"Inequality","level":2,"score":0.3625999987125397},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.35850000381469727},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.3427000045776367},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.335999995470047},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C123046963","wikidata":"https://www.wikidata.org/wiki/Q22664","display_name":"Geographic coordinate system","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.26910001039505005},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.2612000107765198},{"id":"https://openalex.org/C148417208","wikidata":"https://www.wikidata.org/wiki/Q4825882","display_name":"Authentication (law)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2572999894618988},{"id":"https://openalex.org/C76509639","wikidata":"https://www.wikidata.org/wiki/Q918036","display_name":"Race (biology)","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.12659","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.12659","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.12659","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.12659","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.780113697052002}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-Language":[0],"Models":[1],"(VLMs)":[2],"are":[3],"known":[4],"to":[5,38,64,168],"inherit":[6],"and":[7,32,56,62,66,78,97,101,106,119],"amplify":[8],"societal":[9],"biases":[10],"from":[11,94],"their":[12],"web-scale":[13],"training":[14],"data":[15],"with":[16],"Indian":[17,40,176],"being":[18],"particularly":[19],"misrepresented.":[20],"Existing":[21],"fairness-aware":[22],"datasets":[23,154],"have":[24],"significantly":[25],"improved":[26],"demographic":[27],"balance":[28],"across":[29,53,104],"global":[30],"race":[31],"gender":[33],"groups,":[34],"yet":[35],"they":[36],"continue":[37],"treat":[39],"as":[41,145,164],"a":[42,76],"single":[43],"monolithic":[44],"category.":[45],"The":[46],"oversimplification":[47],"ignores":[48],"the":[49,71,133,141,146,165,175],"vast":[50],"intra-national":[51,112],"diversity":[52,87],"28":[54],"states":[55,105],"8":[57],"Union":[58],"Territories":[59],"of":[60,88],"India":[61],"leads":[63],"representational":[65],"geographical":[67,86,113,170],"bias.":[68],"To":[69],"address":[70],"limitation,":[72],"we":[73,110],"present":[74],"IndicFairFace,":[75,109],"novel":[77],"balanced":[79,103],"face":[80],"dataset":[81],"comprising":[82],"14,400":[83],"images":[84],"representing":[85],"India.":[89],"Images":[90],"were":[91],"sourced":[92],"ethically":[93],"Wikimedia":[95],"Commons":[96],"open-license":[98],"web":[99],"repositories":[100],"uniformly":[102],"gender.":[107],"Using":[108],"quantify":[111],"bias":[114,171],"in":[115,149,172],"prominent":[116],"CLIP-based":[117],"VLMs":[118,173],"reduce":[120],"it":[121],"using":[122],"post-hoc":[123],"Iterative":[124],"Nullspace":[125],"Projection":[126],"debiasing":[127,135],"approach.":[128],"We":[129],"also":[130],"show":[131],"that":[132],"adopted":[134],"approach":[136],"does":[137],"not":[138],"adversely":[139],"impact":[140],"existing":[142],"embedding":[143],"space":[144],"average":[147],"drop":[148],"retrieval":[150],"accuracy":[151],"on":[152],"benchmark":[153,167],"is":[155],"less":[156],"than":[157],"1.5":[158],"percent.":[159],"Our":[160],"work":[161],"establishes":[162],"IndicFairFace":[163],"first":[166],"study":[169],"for":[174],"context.":[177]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-17T00:00:00"}