{"id":"https://openalex.org/W7165686522","doi":"https://doi.org/10.48550/arxiv.2606.20628","title":"Human Decision-Making with AI Assistance under Correlated Features","display_name":"Human Decision-Making with AI Assistance under Correlated Features","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7165686522","doi":"https://doi.org/10.48550/arxiv.2606.20628"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.20628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20628","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"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.2606.20628","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122305119","display_name":"Yanru Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guan, Yanru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085536282","display_name":"Naveen Raman","orcid":"https://orcid.org/0009-0006-8886-084X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raman, Naveen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139190432","display_name":"Fei Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Fei","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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.14030000567436218,"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":0.14030000567436218,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.13779999315738678,"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":0.10869999974966049,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/commit","display_name":"Commit","score":0.739300012588501},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.708899974822998},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.600600004196167},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5153999924659729},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.43380001187324524},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.4027999937534332},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.38989999890327454},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.3817000091075897}],"concepts":[{"id":"https://openalex.org/C153180980","wikidata":"https://www.wikidata.org/wiki/Q19776675","display_name":"Commit","level":2,"score":0.739300012588501},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.708899974822998},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.600600004196167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5896999835968018},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5551000237464905},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5153999924659729},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5054000020027161},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.43380001187324524},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.35929998755455017},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2946000099182129},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2912999987602234},{"id":"https://openalex.org/C150325174","wikidata":"https://www.wikidata.org/wiki/Q4335500","display_name":"Optimal decision","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2854999899864197},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.20628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20628","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.20628","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20628","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.803543746471405}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Humans":[0],"increasingly":[1],"make":[2],"decisions":[3],"with":[4,116],"AI":[5,29,93,107],"assistance;":[6],"for":[7,147,157],"example,":[8],"doctors":[9],"may":[10],"follow":[11,87],"AI-recommended":[12],"diagnostic":[13],"tests":[14,27,59,97],"and":[15,36,136,160],"base":[16],"their":[17],"diagnoses":[18],"on":[19,121],"the":[20,57,92,106,122,131,144],"results.":[21],"A":[22],"natural":[23],"question":[24],"is":[25,134],"which":[26],"should":[28,94,108],"recommend":[30,56],"to":[31,75,110,181],"balance":[32],"short-term":[33],"decision":[34],"quality":[35],"long-term":[37],"human":[38],"learning":[39],"when":[40,63],"different":[41],"features":[42,64],"(e.g.,":[43],"test":[44],"results)":[45],"are":[46,61,65],"correlated.":[47],"While":[48],"prior":[49],"work":[50],"establishes":[51],"that":[52,55,69,82,119,129,142,155,176],"stationary":[53,163],"policies":[54,74],"same":[58],"repeatedly":[60],"optimal":[62,84,132,145],"independent,":[66],"we":[67,80],"prove":[68,81,128],"feature":[70,103,125,178],"correlations":[71],"lead":[72],"such":[73],"perform":[76],"arbitrarily":[77],"poorly.":[78],"Instead,":[79],"any":[83],"policy":[85,133,146],"must":[86],"an":[88,153],"explore-then-commit":[89],"structure;":[90],"initially,":[91],"offer":[95],"diverse":[96],"so":[98],"humans":[99],"can":[100],"learn":[101],"accurate":[102],"coefficients,":[104],"then":[105],"commit":[109],"a":[111,138,162],"single":[112],"set":[113],"of":[114,124],"tests,":[115],"exploration":[117,183],"length":[118],"depends":[120],"degree":[123],"correlation.":[126],"We":[127,150],"computing":[130],"NP-hard":[135],"derive":[137],"dynamic":[139],"programming-based":[140],"algorithm":[141],"finds":[143],"finite":[148],"horizons.":[149],"additionally":[151],"develop":[152],"approximation":[154],"plans":[156],"shorter":[158],"horizons":[159],"appends":[161],"suffix,":[164],"achieving":[165],"near-optimal":[166],"performance.":[167],"Our":[168],"empirical":[169],"results":[170],"complement":[171],"our":[172],"theory":[173],"by":[174],"showing":[175],"stronger":[177],"correlation":[179],"leads":[180],"longer":[182],"phases.":[184]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}