{"id":"https://openalex.org/W4280532631","doi":"https://doi.org/10.1145/3531146.3533184","title":"Gender and Racial Bias in Visual Question Answering Datasets","display_name":"Gender and Racial Bias in Visual Question Answering Datasets","publication_year":2022,"publication_date":"2022-06-20","ids":{"openalex":"https://openalex.org/W4280532631","doi":"https://doi.org/10.1145/3531146.3533184"},"language":"en","primary_location":{"id":"doi:10.1145/3531146.3533184","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3531146.3533184","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","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":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3531146.3533184","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068508238","display_name":"Yusuke Hirota","orcid":"https://orcid.org/0000-0002-4661-9584"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"Osaka University","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yusuke Hirota","raw_affiliation_strings":["Osaka University, Japan"],"affiliations":[{"raw_affiliation_string":"Osaka University, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110498147","display_name":"Yuta Nakashima","orcid":null},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"Osaka University","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuta Nakashima","raw_affiliation_strings":["Osaka University, Japan"],"affiliations":[{"raw_affiliation_string":"Osaka University, Japan","institution_ids":["https://openalex.org/I98285908"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028370193","display_name":"Noa Garc\u00eda","orcid":"https://orcid.org/0000-0002-9200-6359"},"institutions":[{"id":"https://openalex.org/I98285908","display_name":"Osaka University","ror":"https://ror.org/035t8zc32","country_code":"JP","type":"education","lineage":["https://openalex.org/I98285908"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Noa Garcia","raw_affiliation_strings":["Osaka University, Japan"],"affiliations":[{"raw_affiliation_string":"Osaka University, Japan","institution_ids":["https://openalex.org/I98285908"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068508238"],"corresponding_institution_ids":["https://openalex.org/I98285908"],"apc_list":null,"apc_paid":null,"fwci":1.7905,"has_fulltext":false,"cited_by_count":33,"citation_normalized_percentile":{"value":0.89838927,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1280","last_page":"1292"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":1.0,"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":1.0,"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":0.9973000288009644,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9818000197410583,"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/racism","display_name":"Racism","score":0.6285964250564575},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6039281487464905},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5977117419242859},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5779088139533997},{"id":"https://openalex.org/keywords/racial-bias","display_name":"Racial bias","score":0.5698199272155762},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5554095506668091},{"id":"https://openalex.org/keywords/gender-bias","display_name":"Gender bias","score":0.5013530254364014},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4979989528656006},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4923783540725708},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45311155915260315},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.43837472796440125},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4042678773403168},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3224920630455017},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.2723342180252075},{"id":"https://openalex.org/keywords/sociology","display_name":"Sociology","score":0.14104774594306946},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10341626405715942}],"concepts":[{"id":"https://openalex.org/C139838865","wikidata":"https://www.wikidata.org/wiki/Q8461","display_name":"Racism","level":2,"score":0.6285964250564575},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6039281487464905},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5977117419242859},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5779088139533997},{"id":"https://openalex.org/C2992700788","wikidata":"https://www.wikidata.org/wiki/Q8461","display_name":"Racial bias","level":3,"score":0.5698199272155762},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5554095506668091},{"id":"https://openalex.org/C2983427547","wikidata":"https://www.wikidata.org/wiki/Q93200","display_name":"Gender bias","level":2,"score":0.5013530254364014},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4979989528656006},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4923783540725708},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45311155915260315},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.43837472796440125},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4042678773403168},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3224920630455017},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.2723342180252075},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.14104774594306946},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10341626405715942},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C107993555","wikidata":"https://www.wikidata.org/wiki/Q1662673","display_name":"Gender studies","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3531146.3533184","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3531146.3533184","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","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":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2205.08148","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.08148","pdf_url":"https://arxiv.org/pdf/2205.08148","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3531146.3533184","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3531146.3533184","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","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":"conference"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1889081078","https://openalex.org/W1949778830","https://openalex.org/W2250384498","https://openalex.org/W2277195237","https://openalex.org/W2302086703","https://openalex.org/W2325636799","https://openalex.org/W2463955103","https://openalex.org/W2560730294","https://openalex.org/W2563399268","https://openalex.org/W2745461083","https://openalex.org/W2770618123","https://openalex.org/W2788481061","https://openalex.org/W2886641317","https://openalex.org/W2916723116","https://openalex.org/W2947312908","https://openalex.org/W2950018712","https://openalex.org/W2955124656","https://openalex.org/W2962749469","https://openalex.org/W2962787423","https://openalex.org/W2963349562","https://openalex.org/W2963518342","https://openalex.org/W2963521239","https://openalex.org/W2963622213","https://openalex.org/W2963644680","https://openalex.org/W2963717374","https://openalex.org/W2966715458","https://openalex.org/W2968124245","https://openalex.org/W2970019270","https://openalex.org/W2970231061","https://openalex.org/W2982232682","https://openalex.org/W2982699810","https://openalex.org/W2990751682","https://openalex.org/W2992319600","https://openalex.org/W2997789966","https://openalex.org/W3010593057","https://openalex.org/W3016211260","https://openalex.org/W3016923549","https://openalex.org/W3016970897","https://openalex.org/W3034656957","https://openalex.org/W3035497460","https://openalex.org/W3035517717","https://openalex.org/W3035651653","https://openalex.org/W3090449556","https://openalex.org/W3091588028","https://openalex.org/W3103455452","https://openalex.org/W3103934428","https://openalex.org/W3127678221","https://openalex.org/W3135474405","https://openalex.org/W3153332739","https://openalex.org/W3172872502","https://openalex.org/W3173220247","https://openalex.org/W3174366544","https://openalex.org/W3177934633","https://openalex.org/W3184784418","https://openalex.org/W3208343573","https://openalex.org/W4286892949","https://openalex.org/W4298392976","https://openalex.org/W4312300614"],"related_works":["https://openalex.org/W22639311","https://openalex.org/W1983325912","https://openalex.org/W2157051509","https://openalex.org/W2905734496","https://openalex.org/W4300198819","https://openalex.org/W2188455416","https://openalex.org/W2605109889","https://openalex.org/W4320467347","https://openalex.org/W2800520335","https://openalex.org/W3081018808"],"abstract_inverted_index":{"Vision-and-language":[0],"tasks":[1],"have":[2,39],"increasingly":[3],"drawn":[4],"more":[5],"attention":[6],"as":[7,139,141],"a":[8,67],"means":[9],"to":[10,31,42,103,174,194],"evaluate":[11],"human-like":[12],"reasoning":[13],"in":[14,21,77,92,116,162],"machine":[15],"learning":[16,47],"models.":[17],"A":[18],"popular":[19],"task":[20],"the":[22,48,58,64,75,78,93,126,142,163,183,189,196,202],"field":[23],"is":[24,80,90,130],"visual":[25],"question":[26],"answering":[27],"(VQA),":[28],"which":[29],"aims":[30],"answer":[32],"questions":[33,52,62,134],"about":[34,63,135],"images.":[35],"However,":[36],"VQA":[37,101,118,176],"models":[38,102],"been":[40],"shown":[41],"exploit":[43],"language":[44],"bias":[45,84,115],"by":[46,191],"statistical":[49],"correlations":[50],"between":[51,133],"and":[53,113,137,180,200],"answers":[54,129],"without":[55,178],"looking":[56],"into":[57],"image":[59,79],"content:":[60],"e.g.,":[61],"color":[65],"of":[66,128,144],"banana":[68,76],"are":[69,155,171],"answered":[70],"with":[71,182],"yellow,":[72],"even":[73],"if":[74],"green.":[81],"If":[82],"societal":[83],"(e.g.,":[85],"sexism,":[86],"racism,":[87],"ableism,":[88],"etc.)":[89],"present":[91],"training":[94],"data,":[95],"this":[96,108],"problem":[97,197],"may":[98],"be":[99],"causing":[100],"learn":[104],"harmful":[105,185],"stereotypes.":[106,186],"For":[107],"reason,":[109],"we":[110,123,149],"investigate":[111],"gender":[112],"racial":[114],"five":[117],"datasets.":[119,165],"In":[120],"our":[121],"analysis,":[122],"find":[124],"that":[125,151,169],"distribution":[127],"highly":[131],"different":[132],"women":[136],"men,":[138],"well":[140],"existence":[143],"detrimental":[145],"gender-stereotypical":[146],"samples.":[147],"Likewise,":[148],"identify":[150],"specific":[152],"race-related":[153],"attributes":[154],"underrepresented,":[156],"whereas":[157],"potentially":[158,184],"discriminatory":[159],"samples":[160],"appear":[161],"analyzed":[164],"Our":[166],"findings":[167],"suggest":[168],"there":[170],"dangers":[172],"associated":[173],"using":[175],"datasets":[177],"considering":[179],"dealing":[181],"We":[187],"conclude":[188],"paper":[190],"proposing":[192],"solutions":[193],"alleviate":[195],"before,":[198],"during,":[199],"after":[201],"dataset":[203],"collection":[204],"process.":[205]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":2}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}