{"id":"https://openalex.org/W4200151635","doi":"https://doi.org/10.1109/istas52410.2021.9629153","title":"Investigating accuracy disparities for gender classification using convolutional neural networks","display_name":"Investigating accuracy disparities for gender classification using convolutional neural networks","publication_year":2021,"publication_date":"2021-10-28","ids":{"openalex":"https://openalex.org/W4200151635","doi":"https://doi.org/10.1109/istas52410.2021.9629153"},"language":"en","primary_location":{"id":"doi:10.1109/istas52410.2021.9629153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/istas52410.2021.9629153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Technology and Society (ISTAS)","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/A5074654501","display_name":"Lia Chin-Purcell","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lia Chin-Purcell","raw_affiliation_strings":["School of Information, UC Berkeley, Berkeley, USA"],"affiliations":[{"raw_affiliation_string":"School of Information, UC Berkeley, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017849947","display_name":"America Chambers","orcid":null},"institutions":[{"id":"https://openalex.org/I146552867","display_name":"University of Puget Sound","ror":"https://ror.org/042drmv40","country_code":"US","type":"education","lineage":["https://openalex.org/I146552867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"America Chambers","raw_affiliation_strings":["Department of Computer Science, University of Puget Sound, Tacoma, WA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Puget Sound, Tacoma, WA","institution_ids":["https://openalex.org/I146552867"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5074654501"],"corresponding_institution_ids":["https://openalex.org/I95457486"],"apc_list":null,"apc_paid":null,"fwci":0.0961,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42218954,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"29","issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.992900013923645,"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/T11448","display_name":"Face recognition and analysis","score":0.992900013923645,"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/T10392","display_name":"Cutaneous Melanoma Detection and Management","score":0.927299976348877,"subfield":{"id":"https://openalex.org/subfields/2730","display_name":"Oncology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/transgender","display_name":"Transgender","score":0.8994884490966797},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7580424547195435},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6504989266395569},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6084700226783752},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5687999129295349},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.522372305393219},{"id":"https://openalex.org/keywords/gender-identity","display_name":"Gender identity","score":0.49843263626098633},{"id":"https://openalex.org/keywords/gender-bias","display_name":"Gender bias","score":0.4901526868343353},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.41460278630256653},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4072042405605316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3577781021595001},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.24150007963180542},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.09787997603416443},{"id":"https://openalex.org/keywords/sociology","display_name":"Sociology","score":0.06810316443443298}],"concepts":[{"id":"https://openalex.org/C2779671885","wikidata":"https://www.wikidata.org/wiki/Q189125","display_name":"Transgender","level":2,"score":0.8994884490966797},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7580424547195435},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6504989266395569},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6084700226783752},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5687999129295349},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.522372305393219},{"id":"https://openalex.org/C2991839931","wikidata":"https://www.wikidata.org/wiki/Q48264","display_name":"Gender identity","level":2,"score":0.49843263626098633},{"id":"https://openalex.org/C2983427547","wikidata":"https://www.wikidata.org/wiki/Q93200","display_name":"Gender bias","level":2,"score":0.4901526868343353},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.41460278630256653},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4072042405605316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3577781021595001},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.24150007963180542},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.09787997603416443},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.06810316443443298},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/istas52410.2021.9629153","is_oa":false,"landing_page_url":"https://doi.org/10.1109/istas52410.2021.9629153","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Symposium on Technology and Society (ISTAS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1849277567","https://openalex.org/W2108598243","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2403084738","https://openalex.org/W2788481061","https://openalex.org/W2899136066","https://openalex.org/W2950018712","https://openalex.org/W2957285709","https://openalex.org/W6639204139","https://openalex.org/W6684191040","https://openalex.org/W6721933647","https://openalex.org/W6748382702"],"related_works":["https://openalex.org/W292570126","https://openalex.org/W3200345166","https://openalex.org/W3016744689","https://openalex.org/W1543657467","https://openalex.org/W4386043655","https://openalex.org/W2944716173","https://openalex.org/W3161577685","https://openalex.org/W2916152643","https://openalex.org/W4285494751","https://openalex.org/W2022919673"],"abstract_inverted_index":{"Automatic":[0],"gender":[1,54],"recognition":[2,9],"(AGR)":[3],"is":[4,108],"a":[5],"subfield":[6],"of":[7,20,84,89,95,129,148],"facial":[8],"that":[10,35,103],"has":[11],"recently":[12],"been":[13],"scrutinized":[14],"for":[15,132,157],"bias":[16,68],"in":[17,28],"the":[18,104,126,149],"form":[19],"misgendering":[21],"and":[22,48,64,74,92,98],"erasure":[23],"against":[24,46,69],"various":[25,146],"identity":[26],"groups":[27],"our":[29,162],"society.":[30],"Recent":[31],"studies":[32],"have":[33],"found":[34],"several":[36],"commercial":[37,158],"AGR":[38,66],"classifiers":[39,159],"(from":[40],"Microsoft,":[41],"IMB,":[42],"Face++)":[43],"are":[44,151],"biased":[45],"women":[47],"darker-skinned":[49],"people":[50,56,71],"as":[51,53],"well":[52],"non-binary":[55],"[8,":[57],"11].":[58],"In":[59],"this":[60,137],"work,":[61],"we":[62],"investigate":[63,136],"quantify":[65],"classifier":[67,107],"transgender":[70,90,99,123,133],"by":[72,141],"developing":[73],"evaluating":[75],"three":[76],"different":[77],"convolutional":[78],"neural":[79],"networks":[80],"(CNN):":[81],"using":[82,87,93],"images":[83,88,94],"cisgender":[85,97,105,114],"individuals,":[86,91],"both":[96],"individuals.":[100],"We":[101,135,153],"find":[102],"trained":[106],"91.7%":[109],"accurate":[110,119],"when":[111,120],"evaluated":[112,121],"on":[113,122],"people,":[115,124],"but":[116],"only":[117],"68.9%":[118],"with":[125,155],"worst":[127],"performance":[128],"38.6%":[130],"precision":[131,139],"men.":[134],"low":[138],"further":[140],"performing":[142],"additional":[143],"experiments":[144],"where":[145],"parts":[147],"face":[150],"obscured.":[152],"end":[154],"recommendations":[156],"based":[160],"upon":[161],"findings.":[163]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
