{"id":"https://openalex.org/W4411541907","doi":"https://doi.org/10.1145/3715275.3732046","title":"Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways","display_name":"Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways","publication_year":2025,"publication_date":"2025-06-23","ids":{"openalex":"https://openalex.org/W4411541907","doi":"https://doi.org/10.1145/3715275.3732046"},"language":"en","primary_location":{"id":"doi:10.1145/3715275.3732046","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3715275.3732046","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3715275.3732046","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3715275.3732046","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002575847","display_name":"Angelina Wang","orcid":"https://orcid.org/0000-0001-9140-3523"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Angelina Wang","raw_affiliation_strings":["Cornell Tech, New York, USA"],"raw_orcid":"https://orcid.org/0000-0001-9140-3523","affiliations":[{"raw_affiliation_string":"Cornell Tech, New York, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014394213","display_name":"Xuechunzi Bai","orcid":"https://orcid.org/0000-0002-2277-5451"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuechunzi Bai","raw_affiliation_strings":["University of Chicago, Chicago, USA"],"raw_orcid":"https://orcid.org/0000-0002-2277-5451","affiliations":[{"raw_affiliation_string":"University of Chicago, Chicago, USA","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074825066","display_name":"Solon Barocas","orcid":"https://orcid.org/0000-0003-4577-466X"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]},{"id":"https://openalex.org/I4401726785","display_name":"Microsoft Research New York City (United States)","ror":"https://ror.org/056zprp28","country_code":null,"type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4401726785"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Solon Barocas","raw_affiliation_strings":["Microsoft Research, New York, USA"],"raw_orcid":"https://orcid.org/0000-0003-4577-466X","affiliations":[{"raw_affiliation_string":"Microsoft Research, New York, USA","institution_ids":["https://openalex.org/I1290206253","https://openalex.org/I4401726785"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001257783","display_name":"Su Lin Blodgett","orcid":"https://orcid.org/0000-0002-9861-3483"},"institutions":[{"id":"https://openalex.org/I4210153468","display_name":"Microsoft (Canada)","ror":"https://ror.org/04xhxg104","country_code":"CA","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210153468"]},{"id":"https://openalex.org/I4402554038","display_name":"Microsoft Research Montr\u00e9al (Canada)","ror":"https://ror.org/05xdft911","country_code":null,"type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4402554038"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Su Lin Blodgett","raw_affiliation_strings":["Microsoft Research, Montreal, Canada"],"raw_orcid":"https://orcid.org/0000-0002-9861-3483","affiliations":[{"raw_affiliation_string":"Microsoft Research, Montreal, Canada","institution_ids":["https://openalex.org/I4210153468","https://openalex.org/I4402554038"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6475,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.90404983,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"746","last_page":"762"},"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":0.9965000152587891,"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":0.9965000152587891,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9860000014305115,"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/T12520","display_name":"Psychology of Moral and Emotional Judgment","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6409984827041626},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42019063234329224},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37961578369140625},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.35734325647354126},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3432600498199463}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6409984827041626},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42019063234329224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37961578369140625},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.35734325647354126},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3432600498199463}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3715275.3732046","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3715275.3732046","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3715275.3732046","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3715275.3732046","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3715275.3732046","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3715275.3732046","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309292","display_name":"Princeton University","ror":"https://ror.org/00hx57361"},{"id":"https://openalex.org/F4320309626","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411541907.pdf","grobid_xml":"https://content.openalex.org/works/W4411541907.grobid-xml"},"referenced_works_count":90,"referenced_works":["https://openalex.org/W1557998370","https://openalex.org/W1964670863","https://openalex.org/W2002598257","https://openalex.org/W2013905719","https://openalex.org/W2086604324","https://openalex.org/W2096372719","https://openalex.org/W2100047082","https://openalex.org/W2100960835","https://openalex.org/W2110020467","https://openalex.org/W2114352794","https://openalex.org/W2116685090","https://openalex.org/W2139095040","https://openalex.org/W2140587370","https://openalex.org/W2141441141","https://openalex.org/W2148905283","https://openalex.org/W2161923363","https://openalex.org/W2290009368","https://openalex.org/W2295585588","https://openalex.org/W2390900624","https://openalex.org/W2563826943","https://openalex.org/W2591615201","https://openalex.org/W2624423453","https://openalex.org/W2626358531","https://openalex.org/W2769358515","https://openalex.org/W2770905023","https://openalex.org/W2793962302","https://openalex.org/W2795743913","https://openalex.org/W2796870183","https://openalex.org/W2798706400","https://openalex.org/W2799258637","https://openalex.org/W2883220905","https://openalex.org/W2889624842","https://openalex.org/W2893425640","https://openalex.org/W2902702261","https://openalex.org/W2911751579","https://openalex.org/W2914202940","https://openalex.org/W2949969209","https://openalex.org/W2954275542","https://openalex.org/W2961388998","https://openalex.org/W2962787423","https://openalex.org/W2962858109","https://openalex.org/W2963526187","https://openalex.org/W2964316623","https://openalex.org/W2972572477","https://openalex.org/W2973192523","https://openalex.org/W2982435932","https://openalex.org/W2990751682","https://openalex.org/W2997588435","https://openalex.org/W3000554516","https://openalex.org/W3014230189","https://openalex.org/W3035241006","https://openalex.org/W3089826743","https://openalex.org/W3100881017","https://openalex.org/W3105882417","https://openalex.org/W3119150429","https://openalex.org/W3157831956","https://openalex.org/W3159059608","https://openalex.org/W3172415559","https://openalex.org/W3172872502","https://openalex.org/W3174356895","https://openalex.org/W3183266055","https://openalex.org/W3193190893","https://openalex.org/W3195725782","https://openalex.org/W3207316473","https://openalex.org/W3211719913","https://openalex.org/W3217152367","https://openalex.org/W4213076034","https://openalex.org/W4213226808","https://openalex.org/W4229005495","https://openalex.org/W4241276195","https://openalex.org/W4245560825","https://openalex.org/W4251062424","https://openalex.org/W4253716955","https://openalex.org/W4255416805","https://openalex.org/W4256679939","https://openalex.org/W4281291869","https://openalex.org/W4283450324","https://openalex.org/W4285090338","https://openalex.org/W4288083516","https://openalex.org/W4321455981","https://openalex.org/W4366549000","https://openalex.org/W4379959055","https://openalex.org/W4382469269","https://openalex.org/W4385565414","https://openalex.org/W4389519898","https://openalex.org/W4391591617","https://openalex.org/W4391823781","https://openalex.org/W4403580174","https://openalex.org/W4404783771","https://openalex.org/W4407689153"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Despite":[0],"a":[1,34,134],"proliferation":[2],"of":[3,18,27,37,141],"research":[4],"on":[5,95,138,147],"the":[6,24,139],"ways":[7],"that":[8,57,145],"machine":[9,49,60,81,142],"learning":[10,50,61,82,143],"models":[11],"can":[12],"propagate":[13],"harmful":[14,121],"stereotypes,":[15],"very":[16],"little":[17],"this":[19],"work":[20],"is":[21,149],"grounded":[22],"in":[23,40,69],"psychological":[25],"experiences":[26],"people":[28,46],"exposed":[29],"to":[30,43,48,55,76,126,129],"such":[31],"stereotypes.We":[32],"use":[33,53],"case":[35],"study":[36],"gender":[38],"stereotypes":[39,64],"image":[41],"search":[42],"examine":[44],"how":[45],"react":[47],"errors.First,":[51],"we":[52,72],"surveys":[54],"show":[56],"not":[58],"all":[59],"errors":[62,86,117],"reflect":[63],"nor":[65],"are":[66,107,113,118],"equally":[67],"harmful.Then,":[68],"experimental":[70],"studies":[71],"randomly":[73],"expose":[74],"participants":[75,105],"stereotypereinforcing,":[77],"-violating,":[78],"and":[79,153],"-neutral":[80],"errors.We":[83],"find":[84],"stereotype-reinforcing":[85],"induce":[87],"more":[88,109,119,135],"experiential":[89,102],"harm,":[90],"while":[91],"having":[92],"minimal":[93],"impact":[94],"participants'":[96],"cognitive":[97],"beliefs,":[98],"attitudes,":[99],"or":[100],"behaviors.This":[101],"harm":[103,152],"impacts":[104],"who":[106,112,148],"women":[108],"than":[110],"those":[111],"men.However,":[114],"certain":[115],"stereotype-violating":[116],"experientially":[120],"for":[122],"men,":[123],"potentially":[124],"due":[125],"perceived":[127],"threats":[128],"masculinity.We":[130],"conclude":[131],"by":[132],"proposing":[133],"nuanced":[136],"perspective":[137],"harms":[140],"errors-one":[144],"depends":[146],"experiencing":[150],"what":[151],"why.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}