{"id":"https://openalex.org/W4416016051","doi":"https://doi.org/10.1145/3746252.3761453","title":"Fairness in Language Models: A Tutorial","display_name":"Fairness in Language Models: A Tutorial","publication_year":2025,"publication_date":"2025-11-07","ids":{"openalex":"https://openalex.org/W4416016051","doi":"https://doi.org/10.1145/3746252.3761453"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761453","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761453","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","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/A5011724269","display_name":"Zichong Wang","orcid":"https://orcid.org/0000-0001-6091-6609"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zichong Wang","raw_affiliation_strings":["Florida International University, Miami, FL, USA"],"raw_orcid":"https://orcid.org/0000-0001-6091-6609","affiliations":[{"raw_affiliation_string":"Florida International University, Miami, FL, USA","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5117614377","display_name":"Avash Palikhe","orcid":"https://orcid.org/0009-0008-2676-3731"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Avash Palikhe","raw_affiliation_strings":["Florida International University, Miami, FL, USA"],"raw_orcid":"https://orcid.org/0009-0008-2676-3731","affiliations":[{"raw_affiliation_string":"Florida International University, Miami, FL, USA","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076425497","display_name":"Zhipeng Yin","orcid":"https://orcid.org/0009-0001-0816-5630"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhipeng Yin","raw_affiliation_strings":["Florida International University, Miami, FL, USA"],"raw_orcid":"https://orcid.org/0009-0001-0816-5630","affiliations":[{"raw_affiliation_string":"Florida International University, Miami, FL, USA","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100710814","display_name":"Wenbin Zhang","orcid":"https://orcid.org/0000-0003-3024-5415"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenbin Zhang","raw_affiliation_strings":["Florida International University, Miami, FL, USA"],"raw_orcid":"https://orcid.org/0000-0003-3024-5415","affiliations":[{"raw_affiliation_string":"Florida International University, Miami, FL, USA","institution_ids":["https://openalex.org/I19700959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011724269"],"corresponding_institution_ids":["https://openalex.org/I19700959"],"apc_list":null,"apc_paid":null,"fwci":2.0379,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90850001,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"6849","last_page":"6852"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.7824000120162964,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.7824000120162964,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.039500001817941666,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.031700000166893005,"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/clarity","display_name":"CLARITY","score":0.6931999921798706},{"id":"https://openalex.org/keywords/trustworthiness","display_name":"Trustworthiness","score":0.5347999930381775},{"id":"https://openalex.org/keywords/taxonomy","display_name":"Taxonomy (biology)","score":0.42579999566078186},{"id":"https://openalex.org/keywords/raising","display_name":"Raising (metalworking)","score":0.4113999903202057},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.4074000120162964},{"id":"https://openalex.org/keywords/grasp","display_name":"GRASP","score":0.40689998865127563},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.2962999939918518}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7450000047683716},{"id":"https://openalex.org/C2777146004","wikidata":"https://www.wikidata.org/wiki/Q14949826","display_name":"CLARITY","level":2,"score":0.6931999921798706},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.5347999930381775},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.45559999346733093},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C2780589192","wikidata":"https://www.wikidata.org/wiki/Q7285140","display_name":"Raising (metalworking)","level":2,"score":0.4113999903202057},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.4074000120162964},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.40689998865127563},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.40630000829696655},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.3499999940395355},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C62989814","wikidata":"https://www.wikidata.org/wiki/Q854648","display_name":"Gossip","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C55587333","wikidata":"https://www.wikidata.org/wiki/Q1133029","display_name":"Engineering ethics","level":1,"score":0.2728999853134155},{"id":"https://openalex.org/C14224292","wikidata":"https://www.wikidata.org/wiki/Q13600188","display_name":"Conceptual framework","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C2781118332","wikidata":"https://www.wikidata.org/wiki/Q430460","display_name":"Capability approach","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761453","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761453","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8767481802","display_name":null,"funder_award_id":"2404039","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2024932032","https://openalex.org/W2978725006","https://openalex.org/W3080638064","https://openalex.org/W3164886736","https://openalex.org/W4321488452","https://openalex.org/W4380366793","https://openalex.org/W4400969772"],"related_works":[],"abstract_inverted_index":{"Language":[0],"Models":[1],"(LMs)":[2],"achieve":[3],"outstanding":[4],"performance":[5],"across":[6],"diverse":[7],"applications":[8],"but":[9],"often":[10],"produce":[11],"biased":[12],"outcomes,":[13],"raising":[14],"concerns":[15,21],"about":[16],"their":[17,86],"trustworthy":[18],"deployment.":[19],"These":[20],"call":[22],"for":[23,103,116,138],"fairness":[24,66,96,117,140],"research":[25,124],"specific":[26],"to":[27,38,53,62,69,84,99,127],"LMs;":[28],"however,":[29],"most":[30],"existing":[31],"work":[32],"in":[33,48,141],"machine":[34],"learning":[35],"assumes":[36],"access":[37],"model":[39],"internals":[40],"or":[41],"training":[42],"data,":[43],"conditions":[44],"that":[45],"rarely":[46],"hold":[47],"practice.":[49],"As":[50],"LMs":[51],"continue":[52],"exert":[54],"growing":[55],"societal":[56],"influence,":[57],"it":[58],"becomes":[59],"increasingly":[60],"important":[61],"understand":[63],"and":[64,89,106,108,135],"address":[65],"challenges":[67],"unique":[68],"these":[70],"models.":[71],"To":[72],"this":[73],"end,":[74],"our":[75],"tutorial":[76,144],"begins":[77],"by":[78,121],"showcasing":[79],"real-world":[80],"examples":[81],"of":[82,113],"bias":[83,104],"highlight":[85],"practical":[87,136],"implications":[88],"uncover":[90],"underlying":[91],"sources.":[92],"We":[93,119],"then":[94],"define":[95],"concepts":[97],"tailored":[98],"LMs,":[100],"review":[101],"methods":[102],"evaluation":[105],"mitigation,":[107],"present":[109],"a":[110],"multi-dimensional":[111],"taxonomy":[112],"benchmark":[114],"datasets":[115],"assessment.":[118],"conclude":[120],"outlining":[122],"open":[123],"challenges,":[125],"aiming":[126],"provide":[128],"the":[129],"community":[130],"with":[131],"both":[132],"conceptual":[133],"clarity":[134],"tools":[137],"fostering":[139],"LMs.":[142],"All":[143],"resources":[145],"are":[146],"publicly":[147],"accessible":[148],"at":[149],"https://github.com/vanbanTruong/fairness-in-large-language-models.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-08T23:25:12.792448","created_date":"2025-11-08T00:00:00"}
