{"id":"https://openalex.org/W7166513093","doi":"https://doi.org/10.1145/3805689.3812208","title":"Do Language Models Pass the Bechdel Test? Auditing Gender Biases in LLM-Generated Screenplays","display_name":"Do Language Models Pass the Bechdel Test? Auditing Gender Biases in LLM-Generated Screenplays","publication_year":2026,"publication_date":"2026-06-25","ids":{"openalex":"https://openalex.org/W7166513093","doi":"https://doi.org/10.1145/3805689.3812208"},"language":null,"primary_location":{"id":"doi:10.1145/3805689.3812208","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805689.3812208","pdf_url":null,"source":null,"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":"Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805689.3812208","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139606114","display_name":"Megha N. Govindu","orcid":"https://orcid.org/0009-0005-3232-2332"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]},{"id":"https://openalex.org/I922845939","display_name":"Philadelphia University","ror":"https://ror.org/03zzmyz63","country_code":"US","type":"education","lineage":["https://openalex.org/I922845939"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Megha N. Govindu","raw_affiliation_strings":["University of Pennsylvania, Philadelphia, USA"],"raw_orcid":"https://orcid.org/0009-0005-3232-2332","affiliations":[{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, USA","institution_ids":["https://openalex.org/I79576946","https://openalex.org/I922845939"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112809271","display_name":"S. Wang","orcid":"https://orcid.org/0009-0002-3437-2227"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]},{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stephanie T. Wang","raw_affiliation_strings":["University of Pennsylvania, University of Pennsylvania, Philadelphia, USA, and Stanford University, Stanford, USA"],"raw_orcid":"https://orcid.org/0009-0002-3437-2227","affiliations":[{"raw_affiliation_string":"University of Pennsylvania, University of Pennsylvania, Philadelphia, USA, and Stanford University, Stanford, USA","institution_ids":["https://openalex.org/I79576946","https://openalex.org/I97018004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086384189","display_name":"Sorelle A. Friedler","orcid":"https://orcid.org/0000-0001-6023-1597"},"institutions":[{"id":"https://openalex.org/I155707491","display_name":"Haverford College","ror":"https://ror.org/04fnrxr62","country_code":"US","type":"education","lineage":["https://openalex.org/I155707491"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sorelle A. Friedler","raw_affiliation_strings":["Haverford College, Haverford, USA"],"raw_orcid":"https://orcid.org/0000-0001-6023-1597","affiliations":[{"raw_affiliation_string":"Haverford College, Haverford, USA","institution_ids":["https://openalex.org/I155707491"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086524212","display_name":"Dana\u00eb Metaxa","orcid":"https://orcid.org/0000-0001-9359-6090"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]},{"id":"https://openalex.org/I922845939","display_name":"Philadelphia University","ror":"https://ror.org/03zzmyz63","country_code":"US","type":"education","lineage":["https://openalex.org/I922845939"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dana\u00e9 Metaxa","raw_affiliation_strings":["University of Pennsylvania, Philadelphia, USA"],"raw_orcid":"https://orcid.org/0000-0001-9359-6090","affiliations":[{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, USA","institution_ids":["https://openalex.org/I79576946","https://openalex.org/I922845939"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.75112238,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"7398","last_page":"7421"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","score":0.1835000067949295,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.1835000067949295,"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/T13173","display_name":"Gender Studies in Language","score":0.11079999804496765,"subfield":{"id":"https://openalex.org/subfields/3318","display_name":"Gender Studies"},"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/T10181","display_name":"Natural Language Processing Techniques","score":0.0414000004529953,"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/audit","display_name":"Audit","score":0.652999997138977},{"id":"https://openalex.org/keywords/gender-bias","display_name":"Gender bias","score":0.2888000011444092},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.25110000371932983}],"concepts":[{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.652999997138977},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.5911999940872192},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.44530001282691956},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.39750000834465027},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3538999855518341},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3337000012397766},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.32710000872612},{"id":"https://openalex.org/C2983427547","wikidata":"https://www.wikidata.org/wiki/Q93200","display_name":"Gender bias","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2621000111103058},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3805689.3812208","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805689.3812208","pdf_url":null,"source":null,"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":"Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2606.24022","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2606.24022","pdf_url":"https://arxiv.org/pdf/2606.24022","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3805689.3812208","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805689.3812208","pdf_url":null,"source":null,"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":"Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.7310900688171387}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W950821216","https://openalex.org/W1602325801","https://openalex.org/W1654577366","https://openalex.org/W1806075696","https://openalex.org/W1966382345","https://openalex.org/W2009706709","https://openalex.org/W2033193852","https://openalex.org/W2045807247","https://openalex.org/W2110346626","https://openalex.org/W2130354913","https://openalex.org/W2158754420","https://openalex.org/W2250280197","https://openalex.org/W2293081954","https://openalex.org/W2295759824","https://openalex.org/W2381162149","https://openalex.org/W2604257330","https://openalex.org/W2758009921","https://openalex.org/W2801736147","https://openalex.org/W2893425640","https://openalex.org/W2898883664","https://openalex.org/W2921199355","https://openalex.org/W2971307358","https://openalex.org/W2987834530","https://openalex.org/W3037831233","https://openalex.org/W3168771811","https://openalex.org/W3184144760","https://openalex.org/W3215475614","https://openalex.org/W4229019986","https://openalex.org/W4306662873","https://openalex.org/W4313127953","https://openalex.org/W4380302072","https://openalex.org/W4387835442","https://openalex.org/W4389519898","https://openalex.org/W4393224518","https://openalex.org/W4399365038","https://openalex.org/W4401947350","https://openalex.org/W4402669759","https://openalex.org/W4403680229","https://openalex.org/W4404024979","https://openalex.org/W4404172978","https://openalex.org/W4404518169","https://openalex.org/W4404781873","https://openalex.org/W4407662298","https://openalex.org/W4411119451","https://openalex.org/W4411119599","https://openalex.org/W4411120774","https://openalex.org/W4411549860","https://openalex.org/W4415654100","https://openalex.org/W4416035570","https://openalex.org/W4416913571"],"related_works":[],"abstract_inverted_index":{"As":[0],"large":[1],"language":[2],"models":[3],"(LLMs)":[4],"are":[5,117],"increasingly":[6],"used":[7],"in":[8,46,69,89,137],"media":[9,167],"production":[10],"from":[11],"journalism":[12],"to":[13,30,37,84,120],"filmmaking,":[14],"what":[15],"impact":[16],"do":[17],"they":[18],"have":[19,141],"on":[20,43],"the":[21,51,60,77,122,159],"stories":[22],"being":[23],"told?":[24],"Prior":[25],"work":[26],"has":[27],"shown":[28],"LLMs":[29,99],"perpetuate":[31],"social":[32,80],"biases,":[33],"including":[34],"those":[35],"related":[36],"gender.":[38],"We":[39,74,92,155],"complement":[40],"existing":[41],"literature":[42],"gender":[44],"bias":[45,88,151],"LLM":[47],"outputs":[48],"by":[49,96,157],"auditing":[50],"network":[52,81,127],"structure":[53],"of":[54,66,79,166],"LLM-generated":[55,90],"movie":[56],"screenplays":[57,94],"through":[58],"automating":[59],"Bechdel":[61,123],"test,":[62],"a":[63],"popular":[64],"measure":[65],"women's":[67],"representation":[68],"literary":[70],"and":[71,104,132,169],"film":[72],"works.":[73],"also":[75],"introduce":[76],"use":[78],"analysis":[82],"measures":[83],"further":[85,163],"analyze":[86],"representational":[87,150],"scripts.":[91],"evaluate":[93],"generated":[95],"three":[97],"state-of-the-art":[98],"(GPT-5,":[100],"Gemini":[101],"3":[102],"Pro,":[103],"Claude":[105],"Sonnet":[106],"4.5)":[107],"against":[108],"768":[109],"corresponding":[110],"human-written":[111,115],"screenplays,":[112],"finding":[113],"that":[114,136],"scripts":[116],"more":[118],"likely":[119],"pass":[121],"test.":[124],"However,":[125],"other":[126],"analyses,":[128],"like":[129],"centrality,":[130],"homophily,":[131],"triadic":[133],"relationships":[134],"demonstrate":[135,148],"some":[138,149],"cases":[139],"LLM-scripts":[140],"less":[142],"bias,":[143],"although":[144],"all":[145],"script":[146],"types":[147],"under":[152],"most":[153],"measures.":[154],"conclude":[156],"discussing":[158],"continued":[160],"need":[161],"for":[162],"quantitative":[164],"assessments":[165],"representations":[168],"AI-generated":[170],"content.":[171]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2026-06-30T00:00:00"}