{"id":"https://openalex.org/W7225188475","doi":"https://doi.org/10.48550/arxiv.2611.02639","title":"Where Is a Prediction Model Miscalibrated, and for Whom? Local Audits in Outcome-Learned Pockets","display_name":"Where Is a Prediction Model Miscalibrated, and for Whom? Local Audits in Outcome-Learned Pockets","publication_year":2026,"publication_date":"2026-11-02","ids":{"openalex":"https://openalex.org/W7225188475","doi":"https://doi.org/10.48550/arxiv.2611.02639"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2611.02639","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.02639","pdf_url":"https://arxiv.org/pdf/2611.02639","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,"listed_in":[],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2611.02639","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077589714","display_name":"Bernardo Modenesi","orcid":"https://orcid.org/0000-0001-8912-6600","observed_orcids":["https://orcid.org/0000-0001-8912-6600"]},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Modenesi, Bernardo","raw_affiliation_strings":["University of Utah , USA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Utah , USA;","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137611648","display_name":"Taoyi Chen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Taoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119991589","display_name":"Sima Najafzadehkhoei","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Najafzadehkhoei, Sima","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160492357","display_name":"Lucia Wang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lucia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5160407728","display_name":"Ameya Diwan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Diwan, Ameya","raw_affiliation_strings":["Google , USA;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Google , USA;","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5143423498","display_name":"Beatrice Knudsen","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Knudsen, Beatrice","raw_affiliation_strings":["University of Utah , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Utah , USA","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.3783999979496002,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.3783999979496002,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.37229999899864197,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.0681999996304512,"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/random-forest-feature-importance","display_name":"random forest feature importance","score":0.5640000104904175},{"id":"https://openalex.org/keywords/ridge-regression","display_name":"ridge regression","score":0.5450000166893005},{"id":"https://openalex.org/keywords/simultaneous-confidence-intervals","display_name":"simultaneous confidence intervals","score":0.8180000185966492},{"id":"https://openalex.org/keywords/icu-mortality-prediction","display_name":"ICU mortality prediction","score":0.8809999823570251},{"id":"https://openalex.org/keywords/chest-x-ray-classification","display_name":"chest X-ray classification","score":0.8870000243186951},{"id":"https://openalex.org/keywords/algorithmic-fairness","display_name":"algorithmic fairness","score":0.5249999761581421}],"concepts":[{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.6491000056266785},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6341000199317932},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6003000140190125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4713999927043915},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.38609999418258667},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3465000092983246},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3418999910354614},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C122383733","wikidata":"https://www.wikidata.org/wiki/Q865920","display_name":"Approximation error","level":2,"score":0.2757999897003174}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2611.02639","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.02639","pdf_url":"https://arxiv.org/pdf/2611.02639","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,"listed_in":[],"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"},{"id":"doi:10.48550/arxiv.2611.02639","doi":"https://doi.org/10.48550/arxiv.2611.02639","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2611.02639","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,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2611.02639","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2611.02639","pdf_url":"https://arxiv.org/pdf/2611.02639","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,"listed_in":[],"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":[{"display_name":"Good health and well-being","score":0.6240000128746033,"id":"https://metadata.un.org/sdg/3"},{"display_name":"Reduced inequalities","score":0.4074000120162964,"id":"https://metadata.un.org/sdg/10"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225188475.pdf","grobid_xml":"https://content.openalex.org/works/W7225188475.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7220972418","https://openalex.org/W7171969313","https://openalex.org/W7160294911","https://openalex.org/W7221611597","https://openalex.org/W7223413578","https://openalex.org/W7220712097","https://openalex.org/W4394644768","https://openalex.org/W4385436489","https://openalex.org/W7164891900","https://openalex.org/W7223753237"],"abstract_inverted_index":{"Overall":[0],"and":[1,4,48,100,123,176,213,216,252],"group-level":[2],"averages,":[3],"ranking":[5],"metrics":[6],"such":[7,60,114],"as":[8,61,115],"the":[9,67,87,117,137,144,172,179,184,191,210,222],"AUC,":[10],"can":[11],"hide":[12],"sets":[13,38],"of":[14,39,168,207,244],"observations":[15,41,72],"for":[16,49,85,110,134,162,225],"which":[17,37,78],"a":[18,32,90,158,228,248,253,260],"prediction":[19,54],"model":[20,33],"systematically":[21],"over-":[22,43],"or":[23,44,64,154],"under-predicts.":[24],"We":[25],"present":[26],"an":[27,131,236],"audit":[28,118,139],"that":[29,108],"asks":[30],"where":[31,221],"is":[34],"miscalibrated":[35],"(in":[36],"similar":[40],"it":[42,141,164,199],"under-predicts":[45],"on":[46,97,102],"average)":[47],"whom":[50],"(whether":[51],"its":[52,83,240],"average":[53],"error":[55,132,160,174,180,204],"differs":[56],"between":[57],"demographic":[58],"groups,":[59],"by":[62,82,195],"race":[63],"sex,":[65],"within":[66,112,231],"same":[68],"set).":[69],"It":[70],"groups":[71],"into":[73],"pockets":[74,212],"using":[75],"random-forest":[76],"similarity,":[77],"weighs":[79],"each":[80],"feature":[81],"importance":[84],"predicting":[86],"outcome,":[88],"with":[89,104,130,150,157,256],"ridge-regression":[91],"grouping":[92,147],"alongside.":[93],"Pockets":[94],"are":[95],"learned":[96],"one":[98,135],"sample":[99],"tested":[101],"another,":[103],"simultaneous":[105],"confidence":[106],"intervals":[107],"allow":[109],"correlation":[111],"units":[113],"hospitals;":[116],"needs":[119],"only":[120],"features,":[121],"outcomes":[122],"fixed":[124],"predictions.":[125],"In":[126,186],"17":[127],"simulation":[128],"scenarios":[129,170],"planted":[133,161],"group,":[136],"combined":[138],"located":[140,239],"better":[142],"than":[143],"strongest":[145],"alternative":[146],"in":[148,166,209,247,259],"15,":[149],"false-alarm":[151],"rates":[152],"near":[153],"below":[155],"5%;":[156],"local":[159,269],"everyone,":[163],"won":[165],"12":[167],"15":[169],"at":[171],"smaller":[173],"size":[175],"lost":[177],"when":[178],"was":[181],"unrelated":[182],"to":[183],"outcome.":[185],"public":[187],"synthetic":[188],"data":[189],"from":[190,227],"care-management":[192],"algorithm":[193],"studied":[194],"Obermeyer":[196],"et":[197],"al.,":[198],"found":[200],"no":[201,272],"detectable":[202,273],"overall":[203,274],"but":[205],"under-prediction":[206],"illness":[208],"sickest":[211],"over-prediction":[214,243],"elsewhere,":[215],"distinguished":[217],"Black":[218],"patients'":[219],"concentration":[220],"score":[223],"under-predicted":[224],"everyone":[226],"Black-White":[229],"gap":[230],"pockets.":[232],"For":[233],"APACHE":[234],"IVa,":[235],"exploratory":[237],"analysis":[238],"previously":[241],"reported":[242],"ICU":[245],"mortality":[246],"mostly":[249],"ventilated":[250],"pocket":[251],"low-risk":[254],"pocket,":[255],"none":[257],"detected":[258],"moderate-risk":[261],"one.":[262],"Two":[263],"chest":[264],"X-ray":[265],"classifiers":[266],"showed":[267],"offsetting":[268],"errors":[270],"behind":[271],"error.":[275],"Code:":[276],"https://github.com/modenesi/pocket-audit":[277]},"counts_by_year":[],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2026-11-04T00:00:00"}
