{"id":"https://openalex.org/W4390684497","doi":"https://doi.org/10.1162/coli_a_00507","title":"A Bayesian Approach to Uncertainty in Word Embedding Bias Estimation","display_name":"A Bayesian Approach to Uncertainty in Word Embedding Bias Estimation","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4390684497","doi":"https://doi.org/10.1162/coli_a_00507"},"language":"en","primary_location":{"id":"doi:10.1162/coli_a_00507","is_oa":true,"landing_page_url":"https://doi.org/10.1162/coli_a_00507","pdf_url":"https://direct.mit.edu/coli/article-pdf/doi/10.1162/coli_a_00507/2207526/coli_a_00507.pdf","source":{"id":"https://openalex.org/S155526855","display_name":"Computational Linguistics","issn_l":"0891-2017","issn":["0891-2017","1530-9312"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Linguistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://direct.mit.edu/coli/article-pdf/doi/10.1162/coli_a_00507/2207526/coli_a_00507.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080698957","display_name":"Alicja Dobrzeniecka","orcid":null},"institutions":[{"id":"https://openalex.org/I865915315","display_name":"Vrije Universiteit Amsterdam","ror":"https://ror.org/008xxew50","country_code":"NL","type":"education","lineage":["https://openalex.org/I865915315"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Alicja Dobrzeniecka","raw_affiliation_strings":["Vrije Universiteit Amsterdam The Netherlands. alizjonizm@gmail.com"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vrije Universiteit Amsterdam The Netherlands. alizjonizm@gmail.com","institution_ids":["https://openalex.org/I865915315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036879704","display_name":"Rafa\u0142 Urbaniak","orcid":"https://orcid.org/0000-0002-6321-2866"},"institutions":[{"id":"https://openalex.org/I40413290","display_name":"University of Gda\u0144sk","ror":"https://ror.org/011dv8m48","country_code":"PL","type":"education","lineage":["https://openalex.org/I40413290"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Rafal Urbaniak","raw_affiliation_strings":["Basis Research Institute, NYC, USA The University of Gdansk, Poland. rfl.urbaniak@gmail.com","The University of Gdansk, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Basis Research Institute, NYC, USA The University of Gdansk, Poland. rfl.urbaniak@gmail.com","institution_ids":["https://openalex.org/I40413290"]},{"raw_affiliation_string":"The University of Gdansk, Poland","institution_ids":["https://openalex.org/I40413290"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9331,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.77371822,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"50","issue":"2","first_page":"563","last_page":"617"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9933000206947327,"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"}},"topics":[{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9933000206947327,"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":0.9835000038146973,"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/T13718","display_name":"Media Influence and Politics","score":0.9782000184059143,"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/computer-science","display_name":"Computer science","score":0.8161537647247314},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.733981192111969},{"id":"https://openalex.org/keywords/bootstrapping","display_name":"Bootstrapping (finance)","score":0.6435964107513428},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.559423565864563},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5428438782691956},{"id":"https://openalex.org/keywords/debiasing","display_name":"Debiasing","score":0.5317686200141907},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5033387541770935},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.44572392106056213},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.44364774227142334},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.42487800121307373},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41791629791259766},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3892565369606018},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3771722912788391},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.322506844997406},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.19043391942977905},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13547998666763306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8161537647247314},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.733981192111969},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.6435964107513428},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.559423565864563},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5428438782691956},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.5317686200141907},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5033387541770935},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.44572392106056213},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.44364774227142334},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.42487800121307373},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41791629791259766},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3892565369606018},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3771722912788391},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.322506844997406},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.19043391942977905},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13547998666763306},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"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/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"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/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1162/coli_a_00507","is_oa":true,"landing_page_url":"https://doi.org/10.1162/coli_a_00507","pdf_url":"https://direct.mit.edu/coli/article-pdf/doi/10.1162/coli_a_00507/2207526/coli_a_00507.pdf","source":{"id":"https://openalex.org/S155526855","display_name":"Computational Linguistics","issn_l":"0891-2017","issn":["0891-2017","1530-9312"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Linguistics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3fa2021ce149444db2ed52baf015a7b1","is_oa":true,"landing_page_url":"https://doaj.org/article/3fa2021ce149444db2ed52baf015a7b1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computational Linguistics, Vol 50, Iss 2 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1162/coli_a_00507","is_oa":true,"landing_page_url":"https://doi.org/10.1162/coli_a_00507","pdf_url":"https://direct.mit.edu/coli/article-pdf/doi/10.1162/coli_a_00507/2207526/coli_a_00507.pdf","source":{"id":"https://openalex.org/S155526855","display_name":"Computational Linguistics","issn_l":"0891-2017","issn":["0891-2017","1530-9312"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320244","host_organization_name":"Association for Computational Linguistics","host_organization_lineage":["https://openalex.org/P4310320244"],"host_organization_lineage_names":["Association for Computational Linguistics"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational Linguistics","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390684497.pdf","grobid_xml":"https://content.openalex.org/works/W4390684497.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1913823733","https://openalex.org/W2027873737","https://openalex.org/W2050482109","https://openalex.org/W2250539671","https://openalex.org/W2487770199","https://openalex.org/W2769358515","https://openalex.org/W2893425640","https://openalex.org/W2912269676","https://openalex.org/W2942160782","https://openalex.org/W2952349219","https://openalex.org/W2954275542","https://openalex.org/W2963078909","https://openalex.org/W2963158254","https://openalex.org/W2964030506","https://openalex.org/W3013547323","https://openalex.org/W3034115845","https://openalex.org/W3034422725","https://openalex.org/W3038331081","https://openalex.org/W3177189402","https://openalex.org/W3184453204","https://openalex.org/W3185212449","https://openalex.org/W3198920343","https://openalex.org/W3212097072","https://openalex.org/W4220826537","https://openalex.org/W4232976691","https://openalex.org/W4243827475","https://openalex.org/W4280548710","https://openalex.org/W4306904459","https://openalex.org/W4385574259","https://openalex.org/W4404783772","https://openalex.org/W6636510571","https://openalex.org/W6721933647","https://openalex.org/W6760413209","https://openalex.org/W6784221162"],"related_works":["https://openalex.org/W4386875279","https://openalex.org/W4362554880","https://openalex.org/W4281684980","https://openalex.org/W2171721708","https://openalex.org/W3214527415","https://openalex.org/W2808284704","https://openalex.org/W4382894884","https://openalex.org/W4286432911","https://openalex.org/W2911655849","https://openalex.org/W3134737443"],"abstract_inverted_index":{"Abstract":[0],"Multiple":[1],"measures,":[2],"such":[3,26,61],"as":[4,39],"WEAT":[5],"or":[6],"MAC,":[7],"attempt":[8],"to":[9,121,154,160],"quantify":[10],"the":[11,29,65,74,129,141,161,175,186],"magnitude":[12],"of":[13,21,104,112,177],"bias":[14,105],"present":[15],"in":[16,19,106],"word":[17,107,126,137,163],"embeddings":[18,108],"terms":[20],"a":[22,70,92,100,156,168],"single-number":[23,178],"metric.":[24],"However,":[25],"metrics":[27],"and":[28,43,124,146,182],"related":[30],"statistical":[31],"significance":[32],"calculations":[33],"rely":[34],"on":[35],"treating":[36],"pre-averaged":[37],"data":[38,41,66],"individual":[40],"points":[42],"utilizing":[44],"bootstrapping":[45],"techniques":[46],"with":[47,133],"low":[48],"sample":[49],"sizes.":[50],"We":[51,139],"show":[52],"that":[53,80],"similar":[54],"results":[55],"can":[56],"be":[57],"easily":[58],"obtained":[59],"using":[60,143],"methods":[62],"even":[63],"if":[64],"are":[67,188],"generated":[68],"by":[69,174],"null":[71],"model":[72],"lacking":[73],"intended":[75],"bias.":[76],"Consequently,":[77],"we":[78,90,118,150],"argue":[79],"this":[81,88],"approach":[82,153],"generates":[83],"false":[84],"confidence.":[85],"To":[86,114],"address":[87],"issue,":[89],"propose":[91],"Bayesian":[93,96],"alternative:":[94],"hierarchical":[95],"modeling,":[97],"which":[98],"enables":[99],"more":[101,169],"uncertainty-sensitive":[102],"inspection":[103],"at":[109],"different":[110],"levels":[111],"granularity.":[113],"showcase":[115],"our":[116,134,152],"method,":[117],"apply":[119],"it":[120],"Religion,":[122],"Gender,":[123],"Race":[125],"lists":[127],"from":[128],"original":[130],"research,":[131],"together":[132],"control":[135],"neutral":[136],"lists.":[138],"deploy":[140],"method":[142],"Google,":[144],"GloVe,":[145],"Reddit":[147,162],"embeddings.":[148],"Further,":[149],"utilize":[151],"evaluate":[155],"debiasing":[157],"technique":[158],"applied":[159],"embedding.":[164],"Our":[165],"findings":[166],"reveal":[167],"complex":[170],"landscape":[171],"than":[172],"suggested":[173],"proponents":[176],"metrics.":[179],"The":[180],"datasets":[181],"source":[183],"code":[184],"for":[185],"paper":[187],"publicly":[189],"available.1":[190]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-08-19T15:00:24.278416","created_date":"2025-10-10T00:00:00"}