{"id":"https://openalex.org/W4391136348","doi":"https://doi.org/10.1145/3641276","title":"Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines","display_name":"Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines","publication_year":2024,"publication_date":"2024-01-23","ids":{"openalex":"https://openalex.org/W4391136348","doi":"https://doi.org/10.1145/3641276"},"language":"en","primary_location":{"id":"doi:10.1145/3641276","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3641276","pdf_url":null,"source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-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/A5033125725","display_name":"John P. Lalor","orcid":"https://orcid.org/0000-0003-0848-4786"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"John P. Lalor","raw_affiliation_strings":["University of Notre Dame, Notre Dame, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016943900","display_name":"Ahmed Abbasi","orcid":"https://orcid.org/0000-0001-7698-7794"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmed Abbasi","raw_affiliation_strings":["University of Notre Dame, Notre Dame, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092964203","display_name":"Kezia Oketch","orcid":"https://orcid.org/0009-0000-1089-2530"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kezia Oketch","raw_affiliation_strings":["University of Notre Dame, Notre Dame, USA"],"affiliations":[{"raw_affiliation_string":"University of Notre Dame, Notre Dame, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100657565","display_name":"Yi Yang","orcid":"https://orcid.org/0000-0001-8863-112X"},"institutions":[{"id":"https://openalex.org/I200769079","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597","country_code":"HK","type":"education","lineage":["https://openalex.org/I200769079"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yi Yang","raw_affiliation_strings":["Hong Kong University of Science and Technology, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Hong Kong University of Science and Technology, Hong Kong, China","institution_ids":["https://openalex.org/I200769079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069657658","display_name":"Nicole Forsgren","orcid":"https://orcid.org/0000-0003-2263-9326"},"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/I58610484","display_name":"Seattle University","ror":"https://ror.org/02jqc0m91","country_code":"US","type":"education","lineage":["https://openalex.org/I58610484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nicole Forsgren","raw_affiliation_strings":["Microsoft Research, Seattle, USA"],"affiliations":[{"raw_affiliation_string":"Microsoft Research, Seattle, USA","institution_ids":["https://openalex.org/I1290206253","https://openalex.org/I58610484"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5033125725"],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":13.1577,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.98731321,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"42","issue":"4","first_page":"1","last_page":"41"},"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":0.9984999895095825,"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":0.9984999895095825,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9858999848365784,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9474999904632568,"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/computer-science","display_name":"Computer science","score":0.8709589242935181},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6966423392295837},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.543369472026825},{"id":"https://openalex.org/keywords/pipeline-transport","display_name":"Pipeline transport","score":0.5138455033302307},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46535736322402954}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8709589242935181},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6966423392295837},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.543369472026825},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.5138455033302307},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46535736322402954},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3641276","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3641276","pdf_url":null,"source":{"id":"https://openalex.org/S4394735545","display_name":"ACM Transactions on Information Systems","issn_l":"1046-8188","issn":["1046-8188","1558-2868"],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Information Systems","raw_type":"journal-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-136103","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-136103","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G787939393","display_name":"III: Small: Collaborative Research: Social Media Based Analysis of Adverse Drug Events: User Modeling, Signal Reliability, and Signal Validation","funder_award_id":"2039915","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":100,"referenced_works":["https://openalex.org/W90362830","https://openalex.org/W1590702148","https://openalex.org/W1819662813","https://openalex.org/W2002258741","https://openalex.org/W2005624335","https://openalex.org/W2006447892","https://openalex.org/W2036885831","https://openalex.org/W2092073019","https://openalex.org/W2119265954","https://openalex.org/W2137704276","https://openalex.org/W2140910804","https://openalex.org/W2153579005","https://openalex.org/W2203167467","https://openalex.org/W2246361553","https://openalex.org/W2250539671","https://openalex.org/W2284729062","https://openalex.org/W2284916459","https://openalex.org/W2325436843","https://openalex.org/W2483215953","https://openalex.org/W2509884321","https://openalex.org/W2530539196","https://openalex.org/W2769358515","https://openalex.org/W2785011159","https://openalex.org/W2802105481","https://openalex.org/W2803901985","https://openalex.org/W2893425640","https://openalex.org/W2909212904","https://openalex.org/W2945976633","https://openalex.org/W2946144951","https://openalex.org/W2949678053","https://openalex.org/W2949969209","https://openalex.org/W2951936974","https://openalex.org/W2956019087","https://openalex.org/W2963341956","https://openalex.org/W2963917042","https://openalex.org/W2964235839","https://openalex.org/W2966284335","https://openalex.org/W2970583189","https://openalex.org/W2978195003","https://openalex.org/W2979826702","https://openalex.org/W3004561114","https://openalex.org/W3006437051","https://openalex.org/W3012624518","https://openalex.org/W3033733989","https://openalex.org/W3035377849","https://openalex.org/W3037831233","https://openalex.org/W3071563765","https://openalex.org/W3089745382","https://openalex.org/W3092103025","https://openalex.org/W3098765837","https://openalex.org/W3101004475","https://openalex.org/W3121452939","https://openalex.org/W3124917443","https://openalex.org/W3133702157","https://openalex.org/W3150796314","https://openalex.org/W3153432523","https://openalex.org/W3158479996","https://openalex.org/W3164008977","https://openalex.org/W3174174150","https://openalex.org/W3181414820","https://openalex.org/W3182641134","https://openalex.org/W3198484931","https://openalex.org/W3202183072","https://openalex.org/W3204674592","https://openalex.org/W3205037653","https://openalex.org/W3209828932","https://openalex.org/W3213670254","https://openalex.org/W3214152503","https://openalex.org/W4200381234","https://openalex.org/W4206807987","https://openalex.org/W4210736086","https://openalex.org/W4213199213","https://openalex.org/W4220774147","https://openalex.org/W4230414599","https://openalex.org/W4233907442","https://openalex.org/W4238846128","https://openalex.org/W4280548710","https://openalex.org/W4282027681","https://openalex.org/W4283830198","https://openalex.org/W4285192297","https://openalex.org/W4285288507","https://openalex.org/W4287890645","https://openalex.org/W4288029087","https://openalex.org/W4294170691","https://openalex.org/W4296448351","https://openalex.org/W4296448463","https://openalex.org/W4299805482","https://openalex.org/W4307539157","https://openalex.org/W4312551924","https://openalex.org/W4315705277","https://openalex.org/W4317036225","https://openalex.org/W4317209756","https://openalex.org/W4317884287","https://openalex.org/W4378835047","https://openalex.org/W4385893817","https://openalex.org/W4388400861","https://openalex.org/W6638208828","https://openalex.org/W6766596047","https://openalex.org/W6782077995","https://openalex.org/W6894028533"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347","https://openalex.org/W4210805261"],"abstract_inverted_index":{"Fairness":[0],"measurement":[1,33],"is":[2,35,301],"crucial":[3],"for":[4,19,55,150,282],"assessing":[5,66],"algorithmic":[6],"bias":[7,68],"in":[8,44,65,69,80,134,140,158,231,274],"various":[9,283],"types":[10],"of":[11,84,94,100,106,137,174,181],"machine":[12],"learning":[13],"(ML)":[14],"models,":[15],"including":[16,286],"ones":[17],"used":[18],"search":[20],"relevance,":[21],"recommendation,":[22],"personalization,":[23],"talent":[24],"analytics,":[25],"and":[26,63,163,171,212,224,247,256,267,293,298],"natural":[27],"language":[28,194],"processing.":[29],"However,":[30],"the":[31,112,123,135,161,168,179,239,263],"fairness":[32,39,153,260],"paradigm":[34],"currently":[36],"dominated":[37],"by":[38,87,117,245],"metrics":[40,76,119,236,249],"that":[41,103,238,257],"examine":[42],"disparities":[43],"allocation":[45],"and/or":[46],"prediction":[47],"error":[48,269],"as":[49,73],"univariate":[50],"key":[51],"performance":[52],"indicators":[53],"(KPIs)":[54],"a":[56,97,107,147],"protected":[57,101,126,156,210],"attribute":[58],"or":[59],"group.":[60],"Although":[61],"important":[62,280],"effective":[64],"ML":[67,85,175,284],"certain":[70],"contexts":[71],"such":[72,138],"recidivism,":[74],"existing":[75,118,235],"don\u2019t":[77],"work":[78],"well":[79],"many":[81],"real-world":[82],"applications":[83],"characterized":[86],"imperfect":[88],"models":[89,139],"applied":[90],"to":[91,131,160,189],"an":[92,303],"array":[93],"instances":[95],"encompassing":[96],"multivariate":[98],"mixture":[99],"attributes,":[102],"are":[104,250],"part":[105],"broader":[108],"process":[109],"pipeline.":[110],"Consequently,":[111],"upstream":[113,169,197,240],"representational":[114,162,241,259],"harm":[115,133,165,242,272],"quantified":[116],"based":[120],"on":[121,185],"how":[122],"model":[124],"represents":[125],"groups":[127],"doesn\u2019t":[128],"necessarily":[129],"relate":[130],"allocational":[132,164,271],"application":[136],"downstream":[141,172,218,275],"policy/decision":[142],"contexts.":[143],"We":[144,177],"propose":[145],"FAIR-Frame,":[146],"model-based":[148],"framework":[149,184],"parsimoniously":[151],"modeling":[152],"across":[154],"multiple":[155],"attributes":[157,211],"regard":[159],"associated":[166,204],"with":[167,205,233,270],"design/development":[170],"usage":[173],"models.":[176,195],"evaluate":[178],"efficacy":[180],"our":[182],"proposed":[183],"two":[186],"testbeds":[187,198,219],"pertaining":[188],"text":[190,294],"classification":[191,215],"using":[192],"pretrained":[193],"The":[196,217],"encompass":[199],"over":[200,225],"fifty":[201],"thousand":[202,207],"documents":[203],"twenty-eight":[206],"users,":[208],"seven":[209],"five":[213],"different":[214,252],"tasks.":[216],"span":[220],"three":[221],"policy":[222],"outcomes":[223],"5.41":[226],"million":[227],"total":[228],"observations.":[229],"Results":[230],"comparison":[232],"several":[234],"show":[237],"measures":[243,261],"produced":[244],"FAIR-Frame":[246],"other":[248],"significantly":[251],"from":[253],"one":[254],"another,":[255],"FAIR-Frame\u2019s":[258],"have":[262,279],"highest":[264],"percentage":[265],"alignment":[266],"lowest":[268],"observed":[273],"applications.":[276],"Our":[277],"findings":[278],"implications":[281],"contexts,":[285],"information":[287],"retrieval,":[288],"user":[289],"modeling,":[290],"digital":[291],"platforms,":[292],"classification,":[295],"where":[296],"responsible":[297],"trustworthy":[299],"AI":[300],"becoming":[302],"imperative.":[304]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":14}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
