{"id":"https://openalex.org/W4283155548","doi":"https://doi.org/10.1145/3531146.3533183","title":"Markedness in Visual Semantic AI","display_name":"Markedness in Visual Semantic AI","publication_year":2022,"publication_date":"2022-06-20","ids":{"openalex":"https://openalex.org/W4283155548","doi":"https://doi.org/10.1145/3531146.3533183"},"language":"en","primary_location":{"id":"doi:10.1145/3531146.3533183","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3531146.3533183","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3531146.3533183","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3531146.3533183","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033282902","display_name":"Robert E. Wolfe","orcid":"https://orcid.org/0000-0002-0915-1855"},"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":["University of Washington, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"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":["University of Washington, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Washington, USA","institution_ids":["https://openalex.org/I201448701"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I201448701"],"apc_list":null,"apc_paid":null,"fwci":2.3959,"has_fulltext":true,"cited_by_count":30,"citation_normalized_percentile":{"value":0.92641783,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1269","last_page":"1279"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9929999709129333,"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/T10028","display_name":"Topic Modeling","score":0.9790999889373779,"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/ethnic-group","display_name":"Ethnic group","score":0.6025314927101135},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.574309229850769},{"id":"https://openalex.org/keywords/race","display_name":"Race (biology)","score":0.5467121601104736},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.5169317126274109},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.5117875933647156},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.16523408889770508},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.15566089749336243},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1266179382801056},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.08947160840034485}],"concepts":[{"id":"https://openalex.org/C137403100","wikidata":"https://www.wikidata.org/wiki/Q41710","display_name":"Ethnic group","level":2,"score":0.6025314927101135},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.574309229850769},{"id":"https://openalex.org/C76509639","wikidata":"https://www.wikidata.org/wiki/Q918036","display_name":"Race (biology)","level":2,"score":0.5467121601104736},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.5169317126274109},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.5117875933647156},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.16523408889770508},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.15566089749336243},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1266179382801056},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.08947160840034485},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3531146.3533183","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3531146.3533183","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3531146.3533183","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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3531146.3533183","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3531146.3533183","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3531146.3533183","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":null,"license_id":null,"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":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5899999737739563}],"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/W4283155548.pdf","grobid_xml":"https://content.openalex.org/works/W4283155548.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1847618513","https://openalex.org/W2072080435","https://openalex.org/W2102085635","https://openalex.org/W2108598243","https://openalex.org/W2114831544","https://openalex.org/W2123024445","https://openalex.org/W2154372415","https://openalex.org/W2194775991","https://openalex.org/W2337002970","https://openalex.org/W2886641317","https://openalex.org/W2913954081","https://openalex.org/W2947075887","https://openalex.org/W2963078909","https://openalex.org/W2971307358","https://openalex.org/W2979826702","https://openalex.org/W2981852735","https://openalex.org/W2990751682","https://openalex.org/W3034115845","https://openalex.org/W3035160371","https://openalex.org/W3094502228","https://openalex.org/W3095351420","https://openalex.org/W3120485916","https://openalex.org/W3133702157","https://openalex.org/W3134095442","https://openalex.org/W3134970617","https://openalex.org/W3185212449","https://openalex.org/W3192706046","https://openalex.org/W3199396412","https://openalex.org/W3204712960","https://openalex.org/W4205667414","https://openalex.org/W4252755926","https://openalex.org/W4285109916","https://openalex.org/W4288089799"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2381483116","https://openalex.org/W2492471733","https://openalex.org/W2348506863","https://openalex.org/W2371917728","https://openalex.org/W3013012681","https://openalex.org/W2007982614","https://openalex.org/W2113257626","https://openalex.org/W2389579140","https://openalex.org/W2512568326"],"abstract_inverted_index":{"We":[0,171],"evaluate":[1],"the":[2,17,27,50,55,87,101,108,119,124,155,177,183,203,212,222,239,244,252,269,277,283,290,321,365,368],"state-of-the-art":[3],"multimodal":[4],"\u201dvisual":[5],"semantic\u201d":[6],"model":[7,80,342],"CLIP":[8,48,178,201,302,338,363],"(\u201dContrastive":[9],"Language":[10],"Image":[11],"Pretraining\u201d)":[12],"for":[13,57,65,96,189,235],"biases":[14,306,366],"related":[15],"to":[16,29,40,85,134,144,162,176,202],"marking":[18],"of":[19,36,54,100,107,126,157,211,224,226,251,268,279,285,289,307,367],"age,":[20,207],"gender,":[21],"and":[22,263,287,309,313,329,331,336,359,370],"race":[23,45],"or":[24,39,46,63,75,77,209,281],"ethnicity.":[25],"Given":[26],"option":[28],"label":[30,43,52,90],"an":[31,114,148,340,355],"image":[32],"as":[33,219],"\u201da":[34],"photo":[35],"a":[37,42,138,231,350],"person\u201d":[38],"select":[41],"denoting":[44,94],"ethnicity,":[47],"chooses":[49],"\u201dperson\u201d":[51,89],"47.9%":[53],"time":[56],"White":[58,262,328],"individuals,":[59,237],"compared":[60,323],"with":[61,137,147,238],"5.0%":[62],"less":[64,142],"individuals":[66,98,105,122,133,153,228,259,275,325,332],"who":[67,260,326,333],"are":[68,128,159,258,261,274,292,317,324,327,334],"Black,":[69],"East":[70],"Asian,":[71,73],"Southeast":[72],"Indian,":[74],"Latino":[76],"Hispanic.":[78],"The":[79,215],"is":[81,116,303,339],"also":[82,111],"more":[83,129,160],"likely":[84,130,143,161],"rank":[86],"unmarked":[88],"higher":[91,194,232],"than":[92,131,168,234,246],"labels":[93],"gender":[95,139,315],"Male":[97,132,169,236,312,330],"(26.7%":[99],"time)":[102],"vs.":[103],"Female":[104,121,152,227,293,314],"(15.2%":[106],"time).":[109],"Age":[110],"affects":[112],"whether":[113],"individual":[115],"marked":[117,136,146,164],"by":[118,181,200],"model:":[120],"under":[123,276],"age":[125,149,156,167,220,248,278,284],"20":[127],"be":[135,145,163],"label,":[140,150],"but":[141],"while":[151,265],"over":[154,282],"40":[158],"based":[165,348],"on":[166,344,349],"individuals.":[170,294],"trace":[172],"our":[173],"results":[174,216,296,360],"back":[175],"embedding":[179],"space":[180],"examining":[182],"self-similarity":[184,195,223,308],"(mean":[185],"pairwise":[186],"cosine":[187],"similarity)":[188],"each":[190],"social":[191,213,256,272],"group,":[192],"where":[193],"denotes":[196],"greater":[197],"attention":[198],"directed":[199],"shared":[204],"characteristics":[205],"(i.e.,":[206],"race,":[208],"gender)":[210],"group.":[214],"indicate":[217,361],"that,":[218],"increases,":[221],"representations":[225],"increases":[229],"at":[230,243],"rate":[233],"disparity":[240],"most":[241,270],"pronounced":[242],"\u201dmore":[245],"70\u201d":[247],"range.":[249],"Six":[250],"ten":[253,267,291],"least":[254],"self-similar":[255,271],"groups":[257,273,316,322],"Male,":[264],"all":[266],"10":[280],"70,":[286],"six":[288],"Our":[295],"yield":[297],"evidence":[298],"that":[299,362],"bias":[300],"in":[301],"intersectional:":[304],"existing":[305],"markedness":[310],"between":[311],"further":[318],"exacerbated":[319],"when":[320],"Black":[335],"Female.":[337],"English-language":[341],"trained":[343],"internet":[345],"content":[346],"gathered":[347],"query":[351],"list":[352],"generated":[353],"from":[354],"American":[356],"website":[357],"(Wikipedia),":[358],"reflects":[364],"language":[369],"society":[371],"which":[372],"produced":[373],"this":[374],"training":[375],"data.":[376]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":2}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}