{"id":"https://openalex.org/W7154607595","doi":"https://doi.org/10.48550/arxiv.2604.13305","title":"Bias at the End of the Score","display_name":"Bias at the End of the Score","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154607595","doi":"https://doi.org/10.48550/arxiv.2604.13305"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.13305","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13305","pdf_url":null,"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.13305","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035721377","display_name":"Salma Abdel Magid","orcid":"https://orcid.org/0000-0001-5791-8128"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Magid, Salma Abdel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063966157","display_name":"Grace Guo","orcid":"https://orcid.org/0000-0001-8733-6268"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Grace","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125914345","display_name":"Esin Tureci","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tureci, Esin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069959743","display_name":"Amaya Dharmasiri","orcid":"https://orcid.org/0000-0002-6190-8317"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dharmasiri, Amaya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073539430","display_name":"V. Ramaswamy","orcid":"https://orcid.org/0000-0002-0552-5338"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramaswamy, Vikram V.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133804246","display_name":"Hanspeter Pfister","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pfister, Hanspeter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133748073","display_name":"Olga Russakovsky","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Russakovsky, Olga","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.2054000049829483,"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":0.2054000049829483,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.11460000276565552,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.07100000232458115,"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/robustness","display_name":"Robustness (evolution)","score":0.7443000078201294},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6328999996185303},{"id":"https://openalex.org/keywords/data-collection","display_name":"Data collection","score":0.5205000042915344},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.47870001196861267},{"id":"https://openalex.org/keywords/data-quality","display_name":"Data quality","score":0.4325000047683716}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7443000078201294},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6328999996185303},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5645999908447266},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.5205000042915344},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.47870001196861267},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.4325000047683716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39340001344680786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34940001368522644},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33230000734329224},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33149999380111694},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3230000138282776},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2969000041484833},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.29499998688697815},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.26429998874664307}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.13305","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13305","pdf_url":null,"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.13305","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13305","pdf_url":null,"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","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":0.7006872892379761}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reward":[0],"models":[1],"(RMs)":[2],"are":[3,34],"inherently":[4],"non-neutral":[5],"value":[6],"functions":[7,90],"designed":[8],"and":[9,53,57,86,112,117,144,162,170],"trained":[10],"to":[11,105,135,173],"encode":[12,128],"specific":[13,64],"objectives,":[14],"such":[15],"as":[16,42,45,88,124,159],"human":[17],"preferences":[18],"or":[19,81],"text-image":[20],"alignment.":[21],"RMs":[22,70,127],"have":[23,75],"become":[24],"crucial":[25],"components":[26],"of":[27,51,60,69,100],"text-to-image":[28],"(T2I)":[29],"generation":[30],"systems":[31],"where":[32],"they":[33],"used":[35],"at":[36],"various":[37],"stages":[38],"for":[39,54,166],"dataset":[40],"filtering,":[41],"evaluation":[43],"metrics,":[44,161],"a":[46,96],"supervisory":[47],"signal":[48],"during":[49,108],"optimization":[50,134],"parameters,":[52],"post-generation":[55],"safety":[56],"quality":[58,125,160],"filtering":[59],"T2I":[61,73,109],"outputs.":[62],"While":[63],"problems":[65],"with":[66,103],"the":[67,72,164],"integration":[68],"into":[71],"pipeline":[74],"been":[76],"studied":[77],"(e.g.":[78],"reward":[79,154],"hacking":[80],"mode":[82],"collapse),":[83],"their":[84,157],"robustness":[85,102],"fairness":[87],"scoring":[89],"remains":[91],"largely":[92],"unknown.":[93],"We":[94,114],"conduct":[95],"large":[97],"scale":[98],"audit":[99],"RM":[101],"respect":[104],"demographic":[106,129,146],"biases":[107],"model":[110],"training":[111,171],"generation.":[113],"provide":[115],"quantitative":[116],"qualitative":[118],"evidence":[119],"that":[120],"while":[121],"originally":[122],"developed":[123],"measures,":[126],"biases,":[130],"which":[131],"cause":[132],"reward-guided":[133],"disproportionately":[136],"sexualize":[137],"female":[138],"image":[139],"subjects":[140],"reinforce":[141],"gender/racial":[142],"stereotypes,":[143],"collapse":[145],"diversity.":[147],"These":[148],"findings":[149],"highlight":[150],"shortcomings":[151],"in":[152],"current":[153],"models,":[155],"challenge":[156],"reliability":[158],"underscore":[163],"need":[165],"improved":[167],"data":[168],"collection":[169],"procedures":[172],"enable":[174],"more":[175],"robust":[176],"scoring.":[177]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-17T00:00:00"}