{"id":"https://openalex.org/W4386242353","doi":"https://doi.org/10.1145/3600211.3604711","title":"Social Biases through the Text-to-Image Generation Lens","display_name":"Social Biases through the Text-to-Image Generation Lens","publication_year":2023,"publication_date":"2023-08-08","ids":{"openalex":"https://openalex.org/W4386242353","doi":"https://doi.org/10.1145/3600211.3604711"},"language":"en","primary_location":{"id":"doi:10.1145/3600211.3604711","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3600211.3604711","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society","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/A5068870408","display_name":"Ranjita Naik","orcid":"https://orcid.org/0009-0001-4120-5251"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ranjita Naik","raw_affiliation_strings":["Microsoft, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011998621","display_name":"Besmira Nushi","orcid":"https://orcid.org/0000-0002-7554-8586"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Besmira Nushi","raw_affiliation_strings":["Microsoft, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft, USA","institution_ids":["https://openalex.org/I1290206253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068870408"],"corresponding_institution_ids":["https://openalex.org/I1290206253"],"apc_list":null,"apc_paid":null,"fwci":16.1918,"has_fulltext":false,"cited_by_count":92,"citation_normalized_percentile":{"value":0.99626061,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"786","last_page":"808"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12650","display_name":"Aesthetic Perception and Analysis","score":0.9825000166893005,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12650","display_name":"Aesthetic Perception and Analysis","score":0.9825000166893005,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9602000117301941,"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/computer-science","display_name":"Computer science","score":0.6070584058761597},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5734822154045105},{"id":"https://openalex.org/keywords/big-five-personality-traits","display_name":"Big Five personality traits","score":0.4767621159553528},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.47108185291290283},{"id":"https://openalex.org/keywords/race","display_name":"Race (biology)","score":0.4702165424823761},{"id":"https://openalex.org/keywords/productivity","display_name":"Productivity","score":0.4587628245353699},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4581098258495331},{"id":"https://openalex.org/keywords/personality","display_name":"Personality","score":0.45646655559539795},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.44674479961395264},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4372060298919678},{"id":"https://openalex.org/keywords/stereotype","display_name":"Stereotype (UML)","score":0.43593621253967285},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4213007092475891},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4138268828392029},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.35776975750923157},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.3290444612503052},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.2670731544494629},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.2509094178676605},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.17017871141433716},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16643881797790527},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15268412232398987},{"id":"https://openalex.org/keywords/sociology","display_name":"Sociology","score":0.13673889636993408},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.10737431049346924}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6070584058761597},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5734822154045105},{"id":"https://openalex.org/C2865642","wikidata":"https://www.wikidata.org/wiki/Q378132","display_name":"Big Five personality traits","level":3,"score":0.4767621159553528},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.47108185291290283},{"id":"https://openalex.org/C76509639","wikidata":"https://www.wikidata.org/wiki/Q918036","display_name":"Race (biology)","level":2,"score":0.4702165424823761},{"id":"https://openalex.org/C204983608","wikidata":"https://www.wikidata.org/wiki/Q2111958","display_name":"Productivity","level":2,"score":0.4587628245353699},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4581098258495331},{"id":"https://openalex.org/C187288502","wikidata":"https://www.wikidata.org/wiki/Q641118","display_name":"Personality","level":2,"score":0.45646655559539795},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.44674479961395264},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4372060298919678},{"id":"https://openalex.org/C168127410","wikidata":"https://www.wikidata.org/wiki/Q1754331","display_name":"Stereotype (UML)","level":2,"score":0.43593621253967285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4213007092475891},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4138268828392029},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.35776975750923157},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3290444612503052},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.2670731544494629},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.2509094178676605},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.17017871141433716},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16643881797790527},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15268412232398987},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.13673889636993408},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.10737431049346924},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"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/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3600211.3604711","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3600211.3604711","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society","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":20,"referenced_works":["https://openalex.org/W2082423681","https://openalex.org/W2099799055","https://openalex.org/W2118573581","https://openalex.org/W2148435699","https://openalex.org/W2149252982","https://openalex.org/W2194775991","https://openalex.org/W2591615201","https://openalex.org/W2967013449","https://openalex.org/W3120485916","https://openalex.org/W3159059608","https://openalex.org/W3166396011","https://openalex.org/W3209190492","https://openalex.org/W4221145068","https://openalex.org/W4224035735","https://openalex.org/W4225307095","https://openalex.org/W4281485151","https://openalex.org/W4283803090","https://openalex.org/W4308619645","https://openalex.org/W4312933868","https://openalex.org/W4319792164"],"related_works":["https://openalex.org/W2492471733","https://openalex.org/W3013012681","https://openalex.org/W2337668750","https://openalex.org/W2348909947","https://openalex.org/W2980238164","https://openalex.org/W4292672442","https://openalex.org/W2496505685","https://openalex.org/W1718051419","https://openalex.org/W2362101859","https://openalex.org/W3210107878"],"abstract_inverted_index":{"Text-to-Image":[0],"(T2I)":[1],"generation":[2,58],"is":[3],"enabling":[4],"new":[5],"applications":[6],"that":[7,53,130,224],"support":[8],"creators,":[9],"designers,":[10],"and":[11,71,89,101,111,124,207,236,247],"general":[12],"end":[13],"users":[14],"of":[15,42,49,96,108,136,142,158,178,199,204,212,244],"productivity":[16],"software":[17],"by":[18,82,154],"generating":[19],"illustrative":[20],"content":[21],"with":[22,194],"high":[23],"photorealism":[24],"starting":[25],"from":[26,144],"a":[27,32,66,196],"given":[28],"descriptive":[29],"text":[30],"as":[31,76],"prompt.":[33],"Such":[34,149],"models":[35],"are":[36,92,234],"however":[37],"trained":[38],"on":[39,84,216],"massive":[40],"amounts":[41],"web":[43],"data,":[44],"which":[45],"surfaces":[46],"the":[47,57,79,156,161,165,179,202],"peril":[48],"potential":[50],"harmful":[51],"biases":[52,75,135,150],"may":[54],"leak":[55],"in":[56,78,160,172,184],"process":[59],"itself.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64,115,188],"take":[65],"multi-dimensional":[67],"approach":[68],"to":[69,239],"studying":[70],"quantifying":[72],"common":[73],"social":[74],"reflected":[77],"generated":[80,229,241],"images,":[81],"focusing":[83],"how":[85],"occupations,":[86],"personality":[87,190],"traits,":[88],"everyday":[90,217],"situations":[91,218],"depicted":[93],"across":[94],"representations":[95,183,215],"(perceived)":[97],"gender,":[98,206],"age,":[99],"race,":[100,205],"geographical":[102,213],"location.":[103],"Through":[104],"an":[105,210],"extensive":[106],"set":[107,198],"both":[109,147],"automated":[110],"human":[112],"evaluation":[113],"experiments":[114],"present":[116],"findings":[117],"for":[118,146,225,242],"two":[119],"popular":[120],"T2I":[121],"models:":[122],"DALLE-v2":[123],"Stable":[125],"Diffusion.":[126],"Our":[127],"results":[128,145],"reveal":[129],"there":[131],"exist":[132],"severe":[133],"occupational":[134],"neutral":[137],"prompts":[138,233],"majorly":[139],"excluding":[140],"groups":[141],"people":[143,200],"models.":[148],"can":[151],"get":[152],"mitigated":[153],"increasing":[155],"amount":[157],"specification":[159],"prompt":[162],"itself,":[163],"although":[164],"prompting":[166],"mitigation":[167],"will":[168],"not":[169],"address":[170],"discrepancies":[171],"image":[173],"quality":[174],"or":[175,181],"other":[176,185],"usages":[177],"model":[180],"its":[182],"scenarios.":[186],"Further,":[187],"observe":[189],"traits":[191],"being":[192],"associated":[193],"only":[195],"limited":[197],"at":[201],"intersection":[203],"age.":[208],"Finally,":[209],"analysis":[211],"location":[214],"(e.g.,":[219],"park,":[220],"food,":[221],"weddings)":[222],"shows":[223],"most":[226],"situations,":[227],"images":[228,240],"through":[230],"default":[231],"location-neutral":[232],"closer":[235],"more":[237],"similar":[238],"locations":[243],"United":[245],"States":[246],"Germany.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":56},{"year":2024,"cited_by_count":30},{"year":2023,"cited_by_count":3}],"updated_date":"2026-04-01T17:29:45.350535","created_date":"2025-10-10T00:00:00"}