{"id":"https://openalex.org/W4383860355","doi":"https://doi.org/10.1145/3600211.3604666","title":"Evaluating Biased Attitude Associations of Language Models in an Intersectional Context","display_name":"Evaluating Biased Attitude Associations of Language Models in an Intersectional Context","publication_year":2023,"publication_date":"2023-08-08","ids":{"openalex":"https://openalex.org/W4383860355","doi":"https://doi.org/10.1145/3600211.3604666"},"language":"en","primary_location":{"id":"doi:10.1145/3600211.3604666","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3600211.3604666","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3600211.3604666","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","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":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3600211.3604666","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027603195","display_name":"Shiva Omrani Sabbaghi","orcid":"https://orcid.org/0000-0001-8637-8685"},"institutions":[{"id":"https://openalex.org/I193531525","display_name":"George Washington University","ror":"https://ror.org/00y4zzh67","country_code":"US","type":"education","lineage":["https://openalex.org/I193531525"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shiva Omrani Sabbaghi","raw_affiliation_strings":["Department of Computer Science, George Washington University, USA"],"raw_orcid":"https://orcid.org/0000-0001-8637-8685","affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Washington University, USA","institution_ids":["https://openalex.org/I193531525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037981078","display_name":"Robert Wolfe","orcid":"https://orcid.org/0000-0001-7133-695X"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Robert Wolfe","raw_affiliation_strings":["Information School, University of Washington, USA"],"raw_orcid":"https://orcid.org/0000-0001-7133-695X","affiliations":[{"raw_affiliation_string":"Information School, University of Washington, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101545719","display_name":"Aylin Caliskan","orcid":"https://orcid.org/0000-0001-7154-8629"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aylin Caliskan","raw_affiliation_strings":["Information School, University of Washington, USA"],"raw_orcid":"https://orcid.org/0000-0001-7154-8629","affiliations":[{"raw_affiliation_string":"Information School, University of Washington, USA","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":18.0059,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.99165752,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"542","last_page":"553"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.9976000189781189,"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"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.9976000189781189,"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/T10028","display_name":"Topic Modeling","score":0.9915000200271606,"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/T10314","display_name":"Social and Intergroup Psychology","score":0.9751999974250793,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.5178642272949219},{"id":"https://openalex.org/keywords/cognitive-bias","display_name":"Cognitive bias","score":0.46253326535224915},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.4612720012664795},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.45543724298477173},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4540690779685974},{"id":"https://openalex.org/keywords/sexual-orientation","display_name":"Sexual orientation","score":0.4527854323387146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4397088289260864},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.43718913197517395},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.41174688935279846},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.3977823853492737},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.35595306754112244},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3478219509124756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2973569631576538}],"concepts":[{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.5178642272949219},{"id":"https://openalex.org/C189216375","wikidata":"https://www.wikidata.org/wiki/Q1127759","display_name":"Cognitive bias","level":3,"score":0.46253326535224915},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.4612720012664795},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.45543724298477173},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4540690779685974},{"id":"https://openalex.org/C2777997956","wikidata":"https://www.wikidata.org/wiki/Q17888","display_name":"Sexual orientation","level":2,"score":0.4527854323387146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4397088289260864},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.43718913197517395},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.41174688935279846},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.3977823853492737},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.35595306754112244},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3478219509124756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2973569631576538},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3600211.3604666","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3600211.3604666","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3600211.3604666","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","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"},{"id":"pmh:oai:arXiv.org:2307.03360","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.03360","pdf_url":"https://arxiv.org/pdf/2307.03360","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-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3600211.3604666","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3600211.3604666","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3600211.3604666","source":null,"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","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"},"sustainable_development_goals":[{"score":0.8100000023841858,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1726082284","display_name":"EXPLOITING ALTERNATE COMPUTING TECHNOLOGIES II\n\n\n\nTHEISS RESEARCH\n\n\n\nPURPOSE: THIS PROPOSAL SEEKS TO MAKE ADVANCES IN TWO SEPARATE AREAS OF EMERGING COMPUTING TECHNOLOGY: QUANTUM INFORMATION SCIENCE AND NEUROMORPHIC COMPUTING. FOR THE FORMER, WORK WILL FOCUS ON THE DEVELOPMENT AND ASSESSMENT OF QUANTUM COMMUNICATION SYSTEMS AND APPLICATIONS. FOR THE LATTER, THE GOAL IS TO IDENTIFY PROMISING APPLICATIONS AND DEVELOP TRAINING METHODS FOR SPIKING NEURAL NETWORKS, AN ALTERNATIVE COMPUTATIONAL SYSTEM LOOSELY INSPIRED BY OUR UNDERSTANDING OF HOW THE HUMAN BRAIN WORKS\n\n\n\nACTIVITIES TO BE PERFORMED: COLLABORATION WITH THE EUROPEAN TELECOMMUNICATIONS STANDARDS INSTITUTE WILL BE UNDERTAKEN TO HELP CLARIFY THE VITAL LINKAGE BETWEEN SECURITY PROOFS FOR IDEALIZED QUANTUM CRYPTOGRAPHIC KEY DISTRIBUTION (QKD) SYSTEMS AND THE CHALLENGES OF REAL SYSTEMS BUILD FROM IMPERFECT COMPONENTS. THE PI WILL ALSO APPLY HIS VAST EXPERIENCE IN DEVELOPING QKD HARDWARE TO THE DEVELOPMENT OF AN EXPERIMENTAL QUANTUM NETWORK IN COLLABORATION WITH NIST SCIENTISTS. FINALLY, IMPROVED METHODS TO TRAIN ARTIFICIAL NEURAL NETWORKS (NN) WILL BE STUDIED, AND THE IMPLEMENTATION OF NN APPLICATIONS AND BENCHMARKS THAT MAP WELL TO SCALABLE, LOW ENERGY NN PLATFORMS SUCH AS THE SUPERCONDUCTING OPTOELECTRONICS NETWORK (SOEN) UNDER STUDY AT NIST WILL BE DEVELOPED.\n\n\n\nEXPECTED OUTCOMES: THE FUNDAMENTAL RESEARCH SUPPORTED BY THIS GRANT WILL CONTRIBUTE TO THE BODY OF KNOWLEDGE NEEDED FOR THE DEVELOPMENT OF PRACTICAL AND RELIABLE SYSTEMS AND APPLICATIONS OF QUANTUM NETWORKS AND SPIKING NEURAL NETWORKS. THE RESULTS OF THIS WORK WILL BE PUBLISHED IN THE OPEN SCIENTIFIC LITERATURE.\n\nINTENDED BENEFICIARIES: THE WORK PROPOSED HERE WILL PROVIDE THE NIST WITH A DEEPER UNDERSTANDING OF THE STATE-OF-THE-ART IN POTENTIAL NEW COMPUTING TECHNOLOGIES, ALLOWING IT TO ANTICIPATE NEEDED MEASUREMENT TECHNIQUES AND TOOLS TO ENABLE DOWNSTREAM COMMERCIALIZATION. WHEN FULLY REALIZED, QUANTUM NETWORKS COULD PROVIDE NEW MEANS OF SECURE INFORMATION EXCHANGE, A WAY TO SCALE CURRENT TODAY?S SMALL QUANTUM COMPONENTS INTO A LARGE, DISTRIBUTED COMPUTING SYSTEM, AS WELL AS TO DEVELOP NEW HIGHLY SENSITIVE SENSING CAPABILITIES. NEUROMORPHIC DEVICES, SUCH AS THE ARTIFICIAL NEURAL NETWORKS TO BE STUDIED HERE, COULD PROVIDE VERY HIGH-SPEED PROCESSING FOR CERTAIN APPLICATIONS WITH VERY LOW POWER REQUIREMENTS, WHICH IS A LIMITING FACTOR IN SCALING UP CONVENTIONAL COMPUTING DEVICES. \n\n\n\nSUBRECIPIENT ACTIVITIES: THERE ARE NO PLANNED SUBAWARDS.","funder_award_id":"60NANB20D212T","funder_id":"https://openalex.org/F4320332178","funder_display_name":"National Institute of Standards and Technology"}],"funders":[{"id":"https://openalex.org/F4320332178","display_name":"National Institute of Standards and Technology","ror":"https://ror.org/05xpvk416"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4383860355.pdf","grobid_xml":"https://content.openalex.org/works/W4383860355.grobid-xml"},"referenced_works_count":80,"referenced_works":["https://openalex.org/W1566289585","https://openalex.org/W1777859530","https://openalex.org/W2023736093","https://openalex.org/W2081756052","https://openalex.org/W2094325539","https://openalex.org/W2098271600","https://openalex.org/W2106279089","https://openalex.org/W2119750321","https://openalex.org/W2121643969","https://openalex.org/W2140534852","https://openalex.org/W2140679639","https://openalex.org/W2141599568","https://openalex.org/W2151543699","https://openalex.org/W2168625032","https://openalex.org/W2250539671","https://openalex.org/W2250653840","https://openalex.org/W2331686956","https://openalex.org/W2493916176","https://openalex.org/W2595782592","https://openalex.org/W2612649659","https://openalex.org/W2769358515","https://openalex.org/W2771976988","https://openalex.org/W2784640584","https://openalex.org/W2794635328","https://openalex.org/W2798357113","https://openalex.org/W2805206884","https://openalex.org/W2893425640","https://openalex.org/W2921633540","https://openalex.org/W2926555354","https://openalex.org/W2950018712","https://openalex.org/W2950939981","https://openalex.org/W2954275542","https://openalex.org/W2959360485","https://openalex.org/W2962739339","https://openalex.org/W2962787423","https://openalex.org/W2963078909","https://openalex.org/W2963341956","https://openalex.org/W2963780471","https://openalex.org/W2965373594","https://openalex.org/W2970597249","https://openalex.org/W2971307358","https://openalex.org/W2972413484","https://openalex.org/W2972668795","https://openalex.org/W2979826702","https://openalex.org/W2981852735","https://openalex.org/W2988217457","https://openalex.org/W2996428491","https://openalex.org/W3034115845","https://openalex.org/W3035241006","https://openalex.org/W3035591180","https://openalex.org/W3103365499","https://openalex.org/W3118781290","https://openalex.org/W3133680136","https://openalex.org/W3157498557","https://openalex.org/W3160492784","https://openalex.org/W3176477796","https://openalex.org/W3185212449","https://openalex.org/W3196813608","https://openalex.org/W3203737321","https://openalex.org/W3204712960","https://openalex.org/W3212496002","https://openalex.org/W3213052799","https://openalex.org/W4221110086","https://openalex.org/W4226191490","https://openalex.org/W4232537966","https://openalex.org/W4283155548","https://openalex.org/W4283793718","https://openalex.org/W4283830198","https://openalex.org/W4288029087","https://openalex.org/W4288058275","https://openalex.org/W4288058287","https://openalex.org/W4288089799","https://openalex.org/W4292779060","https://openalex.org/W4294170691","https://openalex.org/W4294367149","https://openalex.org/W4380319657","https://openalex.org/W4385245566","https://openalex.org/W4385571220","https://openalex.org/W4386249236","https://openalex.org/W4399997615"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W1976179990","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605","https://openalex.org/W3022121105","https://openalex.org/W2053783616"],"abstract_inverted_index":{"Language":[0],"models":[1,46,109,186],"are":[2,41,175],"trained":[3],"on":[4,32,158],"large-scale":[5],"corpora":[6],"that":[7,51,102,107,128,134,187],"embed":[8],"implicit":[9],"biases":[10,58,172],"documented":[11],"in":[12,28,43,124,147,179,206],"psychology.":[13],"Valence":[14],"associations":[15,202],"(pleasantness/unpleasantness)":[16],"of":[17,91,184,203],"social":[18,29,39,72,118],"groups":[19,25,40,204],"determine":[20],"the":[21,84,95,111,129,152,180,201],"biased":[22,113,140],"attitudes":[23,114],"towards":[24],"and":[26,74,120,131,182,211],"concepts":[27],"cognition.":[30],"Building":[31],"this":[33],"established":[34],"literature,":[35],"we":[36,105,135],"quantify":[37,103],"how":[38],"valenced":[42],"English":[44],"language":[45,92,108,185,207],"using":[47],"a":[48,78],"sentence":[49],"template":[50],"provides":[52],"an":[53,159],"intersectional":[54,171],"context.":[55],"We":[56,76,126,150],"study":[57,136],"related":[59],"to":[60,82,98,168,177,195],"age,":[61],"education,":[62],"gender,":[63],"height,":[64],"intelligence,":[65],"literacy,":[66],"race,":[67],"religion,":[68],"sex,":[69],"sexual":[70,121],"orientation,":[71],"class,":[73,119],"weight.":[75],"present":[77],"concept":[79],"projection":[80],"approach":[81,97,165,193],"capture":[83],"valence":[85,161],"subspace":[86],"through":[87],"contextualized":[88],"word":[89],"embeddings":[90],"models.":[93],"Adapting":[94],"projection-based":[96],"embedding":[99],"association":[100],"tests":[101],"bias,":[104],"find":[106,127],"exhibit":[110],"most":[112],"against":[115],"gender":[116],"identity,":[117],"orientation":[122],"signals":[123],"language.":[125],"largest":[130],"better-performing":[132],"model":[133],"is":[137],"also":[138],"more":[139],"as":[141,173,198,209],"it":[142,199],"effectively":[143],"captures":[144],"bias":[145,153],"embedded":[146],"sociocultural":[148],"data.":[149],"validate":[151],"evaluation":[154,162],"method":[155],"by":[156],"overperforming":[157],"intrinsic":[160],"task.":[163],"The":[164],"enables":[166],"us":[167],"measure":[169],"complex":[170],"they":[174],"known":[176],"manifest":[178],"outputs":[181],"applications":[183],"perpetuate":[188],"historical":[189],"biases.":[190],"Moreover,":[191],"our":[192],"contributes":[194],"design":[196],"justice":[197],"studies":[200],"underrepresented":[205],"such":[208],"transgender":[210],"homosexual":[212],"individuals.":[213]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}