{"id":"https://openalex.org/W4283167699","doi":"https://doi.org/10.1145/3531146.3533221","title":"Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness","display_name":"Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness","publication_year":2022,"publication_date":"2022-06-20","ids":{"openalex":"https://openalex.org/W4283167699","doi":"https://doi.org/10.1145/3531146.3533221"},"language":"en","primary_location":{"id":"doi:10.1145/3531146.3533221","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533221","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-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/A5002338764","display_name":"Kate Donahue","orcid":null},"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":true,"raw_author_name":"Kate Donahue","raw_affiliation_strings":["Cornell University, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057438082","display_name":"Alexandra Chouldechova","orcid":"https://orcid.org/0000-0002-2337-9610"},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexandra Chouldechova","raw_affiliation_strings":["Amazon, USA"],"affiliations":[{"raw_affiliation_string":"Amazon, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002843568","display_name":"Krishnaram Kenthapadi","orcid":"https://orcid.org/0000-0003-1237-087X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krishnaram Kenthapadi","raw_affiliation_strings":["Fiddler AI, USA"],"affiliations":[{"raw_affiliation_string":"Fiddler AI, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002338764"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":2.4542,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.89568345,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1639","last_page":"1656"},"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":0.9959999918937683,"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":0.9959999918937683,"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":0.9922000169754028,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.7971880435943604},{"id":"https://openalex.org/keywords/complementarity","display_name":"Complementarity (molecular biology)","score":0.7448602914810181},{"id":"https://openalex.org/keywords/constructive","display_name":"Constructive","score":0.6940664052963257},{"id":"https://openalex.org/keywords/stochastic-game","display_name":"Stochastic game","score":0.50458824634552},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5013551712036133},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4716495871543884},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43985825777053833},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42549800872802734},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10130074620246887}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7971880435943604},{"id":"https://openalex.org/C202269582","wikidata":"https://www.wikidata.org/wiki/Q2644277","display_name":"Complementarity (molecular biology)","level":2,"score":0.7448602914810181},{"id":"https://openalex.org/C2778701210","wikidata":"https://www.wikidata.org/wiki/Q28130034","display_name":"Constructive","level":3,"score":0.6940664052963257},{"id":"https://openalex.org/C22171661","wikidata":"https://www.wikidata.org/wiki/Q1074380","display_name":"Stochastic game","level":2,"score":0.50458824634552},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5013551712036133},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4716495871543884},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43985825777053833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42549800872802734},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10130074620246887},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3531146.3533221","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3531146.3533221","pdf_url":null,"source":{"id":"https://openalex.org/S4363608463","display_name":"2022 ACM Conference on Fairness, Accountability, and Transparency","issn_l":null,"issn":null,"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 ACM Conference on Fairness Accountability and Transparency","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.4399999976158142}],"awards":[{"id":"https://openalex.org/G105238394","display_name":null,"funder_award_id":"DGE-1650441","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":24,"referenced_works":["https://openalex.org/W13924619","https://openalex.org/W2003572022","https://openalex.org/W2124868070","https://openalex.org/W2808450727","https://openalex.org/W2903896358","https://openalex.org/W2942157335","https://openalex.org/W3004483087","https://openalex.org/W3007910004","https://openalex.org/W3012851185","https://openalex.org/W3038401053","https://openalex.org/W3081036064","https://openalex.org/W3104831984","https://openalex.org/W3115164309","https://openalex.org/W3121705224","https://openalex.org/W3128442340","https://openalex.org/W3160458345","https://openalex.org/W3163411042","https://openalex.org/W3163443091","https://openalex.org/W3185727840","https://openalex.org/W4287749605","https://openalex.org/W4287754178","https://openalex.org/W4288058227","https://openalex.org/W4289258088","https://openalex.org/W4310895557"],"related_works":["https://openalex.org/W2521519254","https://openalex.org/W3139833644","https://openalex.org/W3123110765","https://openalex.org/W4383553409","https://openalex.org/W2104948296","https://openalex.org/W1735800226","https://openalex.org/W4285172739","https://openalex.org/W3123208392","https://openalex.org/W2212953222","https://openalex.org/W4372316851"],"abstract_inverted_index":{"Much":[0],"of":[1,31,38,62,145,155,164,174,181],"machine":[2,14],"learning":[3,15],"research":[4],"focuses":[5],"on":[6],"predictive":[7],"accuracy:":[8],"given":[9],"a":[10,13,32,39,56,110,165],"task,":[11],"create":[12],"model":[16,133],"(or":[17],"algorithm)":[18],"that":[19,68,91,120],"maximizes":[20],"accuracy.":[21],"In":[22,167],"many":[23],"settings,":[24],"however,":[25],"the":[26,36,81,153,162,178],"final":[27],"prediction":[28],"or":[29,77,83],"decision":[30],"system":[33],"is":[34,66,139,148],"under":[35],"control":[37],"human,":[40],"who":[41],"uses":[42],"an":[43],"algorithm\u2019s":[44],"output":[45],"along":[46],"with":[47,159],"their":[48],"own":[49],"personal":[50],"expertise":[51],"in":[52,93],"order":[53],"to":[54,70,134,161],"produce":[55,71],"combined":[57,179],"prediction.":[58],"One":[59],"ultimate":[60],"goal":[61],"such":[63],"collaborative":[64,195],"systems":[65,117],"complementarity:":[67],"is,":[69],"lower":[72],"loss":[73],"(equivalently,":[74],"greater":[75],"payoff":[76],"utility)":[78],"than":[79],"either":[80],"human":[82],"algorithm":[84],"alone.":[85],"However,":[86],"experimental":[87],"results":[88,170],"have":[89],"shown":[90],"even":[92],"carefully-designed":[94],"systems,":[95,183],"complementary":[96],"performance":[97,180],"can":[98,124,190],"be":[99,125,192],"elusive.":[100],"Our":[101],"work":[102],"provides":[103],"three":[104],"key":[105,175],"contributions.":[106],"First,":[107],"we":[108,130,151],"provide":[109],"theoretical":[111],"framework":[112],"for":[113,194],"modeling":[114],"simple":[115],"human-algorithm":[116,182],"and":[118,141],"demonstrate":[119],"multiple":[121],"prior":[122],"analyses":[123],"expressed":[126],"within":[127],"it.":[128],"Next,":[129],"use":[131],"this":[132],"prove":[135],"conditions":[136],"where":[137,146],"complementarity":[138,147],"impossible,":[140],"give":[142],"constructive":[143],"examples":[144],"achievable.":[149],"Finally,":[150],"discuss":[152],"implications":[154],"our":[156,172],"findings,":[157],"especially":[158],"respect":[160],"fairness":[163],"classifier.":[166],"sum,":[168],"these":[169],"deepen":[171],"understanding":[173],"factors":[176],"influencing":[177],"giving":[184],"insight":[185],"into":[186],"how":[187],"algorithmic":[188],"tools":[189],"best":[191],"designed":[193],"environments.":[196]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
