{"id":"https://openalex.org/W4387171583","doi":"https://doi.org/10.3233/faia230473","title":"Adversarial Discriminator to Mitigate Gender Bias in Abusive Language Detection","display_name":"Adversarial Discriminator to Mitigate Gender Bias in Abusive Language Detection","publication_year":2023,"publication_date":"2023-09-28","ids":{"openalex":"https://openalex.org/W4387171583","doi":"https://doi.org/10.3233/faia230473"},"language":"en","primary_location":{"id":"doi:10.3233/faia230473","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3233/faia230473","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230473","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230473","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072149006","display_name":"J.S. Park","orcid":"https://orcid.org/0000-0003-3124-1651"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaeil Park","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Seoul 03722, South Korea"],"raw_orcid":"https://orcid.org/0000-0003-3124-1651","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Seoul 03722, South Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102775641","display_name":"Sung-Bae Cho","orcid":"https://orcid.org/0000-0002-0185-1769"},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Sung-Bae Cho","raw_affiliation_strings":["Department of Computer Science, Yonsei University, Seoul 03722, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-0185-1769","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Yonsei University, Seoul 03722, South Korea","institution_ids":["https://openalex.org/I193775966"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5102775641"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.4988,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.68069582,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9995999932289124,"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":0.9995999932289124,"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/discriminator","display_name":"Discriminator","score":0.8858034014701843},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6388779282569885},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5522413849830627},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5133118033409119},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.48130834102630615},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4797321856021881},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4429197907447815},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36635881662368774},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.21786177158355713},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.17750713229179382}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8858034014701843},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6388779282569885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5522413849830627},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5133118033409119},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.48130834102630615},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4797321856021881},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4429197907447815},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36635881662368774},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.21786177158355713},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.17750713229179382},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia230473","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3233/faia230473","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230473","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia230473","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3233/faia230473","pdf_url":"https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA230473","source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4300000071525574}],"awards":[{"id":"https://openalex.org/G1478288009","display_name":null,"funder_award_id":"2021-0-02068","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"},{"id":"https://openalex.org/G4643994530","display_name":null,"funder_award_id":"2021-0-02068","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G4700831490","display_name":null,"funder_award_id":"2022-","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G525053333","display_name":null,"funder_award_id":"2022-0-00113","funder_id":"https://openalex.org/F4320335489","funder_display_name":"Institute for Information and Communications Technology Promotion"},{"id":"https://openalex.org/G8652231586","display_name":null,"funder_award_id":"No. 2021-0-02068","funder_id":"https://openalex.org/F4320328359","funder_display_name":"Ministry of Science and ICT, South Korea"}],"funders":[{"id":"https://openalex.org/F4320321314","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"},{"id":"https://openalex.org/F4320335489","display_name":"Institute for Information and Communications Technology Promotion","ror":"https://ror.org/01g0hqq23"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387171583.pdf","grobid_xml":"https://content.openalex.org/works/W4387171583.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W2340954483","https://openalex.org/W2473555522","https://openalex.org/W2483215953","https://openalex.org/W2557579533","https://openalex.org/W2785615365","https://openalex.org/W2785961484","https://openalex.org/W2791170418","https://openalex.org/W2796868841","https://openalex.org/W2807251972","https://openalex.org/W2888167352","https://openalex.org/W2904539038","https://openalex.org/W2911489562","https://openalex.org/W2922580172","https://openalex.org/W2947283491","https://openalex.org/W2949969209","https://openalex.org/W2950018712","https://openalex.org/W2950029751","https://openalex.org/W2950888501","https://openalex.org/W2953553271","https://openalex.org/W2962990575","https://openalex.org/W2963526187","https://openalex.org/W2970583189","https://openalex.org/W2970636124","https://openalex.org/W2972644974","https://openalex.org/W2980350050","https://openalex.org/W2998463583","https://openalex.org/W3016682301","https://openalex.org/W3035241006","https://openalex.org/W3035591180","https://openalex.org/W3035671939","https://openalex.org/W3039559565","https://openalex.org/W3087077382","https://openalex.org/W3095105395","https://openalex.org/W3100881017","https://openalex.org/W3101934021","https://openalex.org/W3104142662","https://openalex.org/W3112497262","https://openalex.org/W3113270693","https://openalex.org/W3131790554","https://openalex.org/W3164886736","https://openalex.org/W3173628907","https://openalex.org/W3198943295","https://openalex.org/W4221147765","https://openalex.org/W4223894231","https://openalex.org/W4229008248","https://openalex.org/W4280570554","https://openalex.org/W4284899401","https://openalex.org/W4285078001","https://openalex.org/W4285146702","https://openalex.org/W4285152678","https://openalex.org/W4287855127","https://openalex.org/W4288029087","https://openalex.org/W4293469690","https://openalex.org/W4293568373","https://openalex.org/W4300485781","https://openalex.org/W4327652274"],"related_works":["https://openalex.org/W4293202849","https://openalex.org/W1980965563","https://openalex.org/W1489300767","https://openalex.org/W4380714744","https://openalex.org/W2387995142","https://openalex.org/W4319453655","https://openalex.org/W2089959425","https://openalex.org/W2596763562","https://openalex.org/W2964218010","https://openalex.org/W4321789545"],"abstract_inverted_index":{"Abusive":[0],"language":[1,186],"detection":[2,48],"models":[3],"tend":[4,35],"to":[5,28,36],"have":[6],"a":[7,53,85,92],"gender":[8,24,58,86,96,105,127,146,177],"bias":[9,59,168,178],"problem":[10],"in":[11,39,46,175,179],"which":[12],"the":[13,95,101,104,108,111,132,137,140,145,157,161,165],"model":[14],"is":[15],"biased":[16],"towards":[17],"sentences":[18],"containing":[19],"identity":[20],"words":[21],"of":[22,139,150],"specific":[23],"groups.":[25],"Previous":[26],"studies":[27],"reduce":[29],"bias,":[30],"such":[31],"as":[32],"projection":[33],"methods,":[34],"lose":[37],"information":[38,63,71,119,142],"word":[40],"vectors":[41,68,74,115],"and":[42,91,107,125,143,152,171],"sentence":[43,66],"context,":[44],"resulting":[45],"low":[47],"accuracy.":[49],"This":[50],"paper":[51],"proposes":[52],"novel":[54],"method":[55,134,159],"that":[56,123,131],"mitigates":[57,126],"while":[60],"preserving":[61],"original":[62],"by":[64,76],"regularizing":[65],"embedding":[67,153],"based":[69],"on":[70],"theory.":[72],"Latent":[73,114],"generated":[75],"an":[77,88],"autoencoder":[78],"are":[79,99,116],"debiased":[80],"through":[81,118,148],"dual":[82],"regularization":[83],"using":[84],"discriminator,":[87],"abuse":[89,112],"classifier,":[90],"decoder.":[93],"While":[94],"discriminator":[97,102],"labels":[98],"randomized,":[100],"confuses":[103],"feature,":[106],"classifier":[109],"retains":[110],"information.":[113],"regularized":[117],"theoretic":[120],"adversarial":[121],"optimization":[122],"disentangles":[124],"features.":[128],"We":[129],"show":[130],"proposed":[133,158],"successfully":[135],"orthogonalizes":[136],"direction":[138],"correlated":[141],"reduces":[144],"feature":[147],"calculation":[149],"subspaces":[151],"vector":[154],"visualization.":[155],"Moreover,":[156],"maintains":[160],"highest":[162],"accuracy":[163],"among":[164],"four":[166,180],"state-of-the-art":[167],"mitigation":[169],"methods":[170],"shows":[172],"superior":[173],"performance":[174],"reducing":[176],"different":[181],"Twitter":[182],"datasets":[183],"for":[184],"abusive":[185],"detection.":[187]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
