{"id":"https://openalex.org/W3035680038","doi":"https://doi.org/10.1145/3397271.3401280","title":"Do Neural Ranking Models Intensify Gender Bias?","display_name":"Do Neural Ranking Models Intensify Gender Bias?","publication_year":2020,"publication_date":"2020-07-25","ids":{"openalex":"https://openalex.org/W3035680038","doi":"https://doi.org/10.1145/3397271.3401280","mag":"3035680038"},"language":"en","primary_location":{"id":"doi:10.1145/3397271.3401280","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401280","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2005.00372","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Navid Rekabsaz","orcid":null},"institutions":[{"id":"https://openalex.org/I121883995","display_name":"Johannes Kepler University of Linz","ror":"https://ror.org/052r2xn60","country_code":"AT","type":"education","lineage":["https://openalex.org/I121883995"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Navid Rekabsaz","raw_affiliation_strings":["Johannes Kepler University, Linz, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johannes Kepler University, Linz, Austria","institution_ids":["https://openalex.org/I121883995"]}]},{"author_position":"last","author":{"id":null,"display_name":"Markus Schedl","orcid":null},"institutions":[{"id":"https://openalex.org/I121883995","display_name":"Johannes Kepler University of Linz","ror":"https://ror.org/052r2xn60","country_code":"AT","type":"education","lineage":["https://openalex.org/I121883995"]}],"countries":["AT"],"is_corresponding":false,"raw_author_name":"Markus Schedl","raw_affiliation_strings":["Johannes Kepler University, Linz, Austria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Johannes Kepler University, Linz, Austria","institution_ids":["https://openalex.org/I121883995"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I121883995"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2065","last_page":"2068"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9984999895095825,"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/T10028","display_name":"Topic Modeling","score":0.9984999895095825,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.9876999855041504,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.9825000166893005,"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/ranking","display_name":"Ranking (information retrieval)","score":0.7421000003814697},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5968999862670898},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5174999833106995},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.5019000172615051},{"id":"https://openalex.org/keywords/degree","display_name":"Degree (music)","score":0.41119998693466187},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4104999899864197}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.7421000003814697},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6136000156402588},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5968999862670898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5421000123023987},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5174999833106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5029000043869019},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5019000172615051},{"id":"https://openalex.org/C2775997480","wikidata":"https://www.wikidata.org/wiki/Q586277","display_name":"Degree (music)","level":2,"score":0.41119998693466187},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38679999113082886},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3375999927520752},{"id":"https://openalex.org/C2983427547","wikidata":"https://www.wikidata.org/wiki/Q93200","display_name":"Gender bias","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.3059999942779541},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2944999933242798},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2590000033378601}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3397271.3401280","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401280","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2005.00372","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.00372","pdf_url":"https://arxiv.org/pdf/2005.00372","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2005.00372","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.00372","pdf_url":"https://arxiv.org/pdf/2005.00372","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2149252982","https://openalex.org/W2250539671","https://openalex.org/W2286300105","https://openalex.org/W2544318541","https://openalex.org/W2591615201","https://openalex.org/W2648699835","https://openalex.org/W2783640434","https://openalex.org/W2796402109","https://openalex.org/W2799258637","https://openalex.org/W2888161220","https://openalex.org/W2926555354","https://openalex.org/W2964012472","https://openalex.org/W4300482433"],"related_works":[],"abstract_inverted_index":{"Concerns":[0],"regarding":[1],"the":[2,29,32,57,60,80,97,106,131,137,156],"footprint":[3],"of":[4,31,34,59,63,79,86,109,159],"societal":[5],"biases":[6],"in":[7,15,37,66,135,155],"information":[8],"retrieval":[9,39,101],"(IR)":[10],"systems":[11],"have":[12],"been":[13],"raised":[14],"several":[16,114],"previous":[17],"studies.":[18],"In":[19],"this":[20,42],"work,":[21],"we":[22,44,82,103],"examine":[23,74],"various":[24],"recent":[25,115],"IR":[26,69,75],"models":[27,76,125,161],"from":[28],"perspective":[30],"degree":[33,58],"gender":[35,107,146,157],"bias":[36,48,108,158],"their":[38],"results.":[40],"To":[41,73],"end,":[43],"first":[45],"provide":[46],"a":[47,67,84,110],"measurement":[49],"framework":[50],"which":[51],"includes":[52],"two":[53],"metrics":[54],"to":[55,96],"quantify":[56],"unbalanced":[61],"presence":[62],"gender-related":[64],"concepts":[65],"given":[68],"model's":[70],"ranking":[71,117],"list.":[72],"by":[77,90],"means":[78],"framework,":[81],"create":[83],"dataset":[85],"non-gendered":[87],"queries,":[88],"selected":[89],"human":[91],"annotators.":[92],"Applying":[93],"these":[94],"queries":[95],"MS":[98],"MARCO":[99],"Passage":[100],"collection,":[102],"then":[104],"measure":[105],"BM25":[111],"model":[112],"and":[113,134],"neural":[116,132,160],"models.":[118],"The":[119],"results":[120],"show":[121,151],"that":[122],"while":[123],"all":[124],"are":[126],"strongly":[127],"biased":[128],"toward":[129],"male,":[130],"models,":[133,143],"particular":[136],"ones":[138],"based":[139],"on":[140],"contextualized":[141],"embedding":[142],"significantly":[144],"intensify":[145],"bias.":[147],"Our":[148],"experiments":[149],"also":[150],"an":[152],"overall":[153],"increase":[154],"when":[162,168],"they":[163,169],"exploit":[164],"transfer":[165],"learning,":[166],"namely":[167],"use":[170],"(already":[171],"biased)":[172],"pre-trained":[173],"embeddings.":[174]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2020-06-19T00:00:00"}