{"id":"https://openalex.org/W3168054285","doi":"https://doi.org/10.1145/3463677.3463714","title":"The Right Tool for The Job? Assessing the Use of Artificial Intelligence for Identifying Administrative Errors","display_name":"The Right Tool for The Job? Assessing the Use of Artificial Intelligence for Identifying Administrative Errors","publication_year":2021,"publication_date":"2021-06-09","ids":{"openalex":"https://openalex.org/W3168054285","doi":"https://doi.org/10.1145/3463677.3463714","mag":"3168054285"},"language":"en","primary_location":{"id":"doi:10.1145/3463677.3463714","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3463677.3463714","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"DG.O2021: The 22nd Annual International Conference on Digital Government Research","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/A5071407605","display_name":"Matthew Young","orcid":"https://orcid.org/0000-0002-9262-2346"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matthew Young","raw_affiliation_strings":["Syracuse University, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, United States","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010707458","display_name":"Johannes Himmelreich","orcid":"https://orcid.org/0000-0002-2163-0082"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Johannes Himmelreich","raw_affiliation_strings":["Syracuse University, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, United States","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036606014","display_name":"Danylo Honcharov","orcid":null},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Danylo Honcharov","raw_affiliation_strings":["Syracuse University, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, United States","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062221607","display_name":"Sucheta Soundarajan","orcid":"https://orcid.org/0000-0003-1166-4067"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sucheta Soundarajan","raw_affiliation_strings":["Syracuse University, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, United States","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8981,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.76704252,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"15","last_page":"26"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9517999887466431,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.9517999887466431,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11762","display_name":"Law, Economics, and Judicial Systems","score":0.9495999813079834,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9451000094413757,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7186443209648132},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6919636726379395},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6477190256118774},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6365477442741394},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.635303258895874},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6338691711425781},{"id":"https://openalex.org/keywords/unemployment","display_name":"Unemployment","score":0.5443488955497742},{"id":"https://openalex.org/keywords/payment","display_name":"Payment","score":0.5429023504257202},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5312485694885254},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.21626988053321838}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7186443209648132},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6919636726379395},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6477190256118774},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6365477442741394},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.635303258895874},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6338691711425781},{"id":"https://openalex.org/C2778126366","wikidata":"https://www.wikidata.org/wiki/Q41171","display_name":"Unemployment","level":2,"score":0.5443488955497742},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.5429023504257202},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5312485694885254},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.21626988053321838},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3463677.3463714","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3463677.3463714","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"DG.O2021: The 22nd Annual International Conference on Digital Government Research","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1531867399","https://openalex.org/W1924689489","https://openalex.org/W1965859051","https://openalex.org/W1967689709","https://openalex.org/W1970441279","https://openalex.org/W2030537285","https://openalex.org/W2060869667","https://openalex.org/W2087016914","https://openalex.org/W2087347434","https://openalex.org/W2088794999","https://openalex.org/W2099270437","https://openalex.org/W2119266541","https://openalex.org/W2129018774","https://openalex.org/W2136132422","https://openalex.org/W2163098330","https://openalex.org/W2475334473","https://openalex.org/W2561283532","https://openalex.org/W2565516711","https://openalex.org/W2619680714","https://openalex.org/W2619904157","https://openalex.org/W2734863171","https://openalex.org/W2756814074","https://openalex.org/W2782767340","https://openalex.org/W2788007910","https://openalex.org/W2794173404","https://openalex.org/W2896833840","https://openalex.org/W2897702578","https://openalex.org/W2947606687","https://openalex.org/W2964022491","https://openalex.org/W2964182926","https://openalex.org/W2982172486","https://openalex.org/W3016154458","https://openalex.org/W3034235662","https://openalex.org/W3091163231","https://openalex.org/W3093679084","https://openalex.org/W3153298903"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W4386690025","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W2073883415","https://openalex.org/W4289703016"],"abstract_inverted_index":{"This":[0,38,134],"article":[1,86],"explores":[2],"the":[3,47,50,72,82,94,102,140,143,148],"extent":[4],"to":[5,12,29,67,100,161],"which":[6,26],"machine":[7,144],"learning":[8,55,109,131,145,153],"can":[9],"be":[10,137,156],"used":[11],"detect":[13],"administrative":[14,19],"errors.":[15],"It":[16],"concentrates":[17],"on":[18],"errors":[20],"in":[21,46],"unemployment":[22],"insurance":[23],"(UI)":[24],"decisions,":[25],"give":[27],"rise":[28],"a":[30,114],"public":[31],"values":[32],"conflict":[33,39,61],"between":[34,142],"efficiency":[35],"and":[36,43,70,107,147],"effectiveness.":[37],"is":[40,122],"first":[41],"described":[42],"then":[44],"highlighted":[45],"history":[48],"of":[49,74,104],"US":[51,95],"UI":[52,91],"regime.":[53],"Machine":[54],"may":[56,64],"not":[57],"only":[58],"mitigate":[59],"this":[60],"but":[62],"it":[63],"also":[65],"help":[66],"combat":[68],"fraud":[69],"reduce":[71],"backlog":[73],"claims":[75,163],"associated":[76],"with":[77],"economic":[78],"crises":[79],"such":[80],"as":[81],"COVID-19":[83],"pandemic.":[84],"The":[85],"uses":[87],"data":[88],"about":[89],"improper":[90],"payments":[92],"throughout":[93],"from":[96],"2002":[97],"through":[98],"2018":[99],"analyze":[101],"accuracy":[103],"random":[105,115],"forests":[106],"deep":[108,130,152],"models.":[110],"We":[111],"find":[112],"that":[113],"forest":[116],"model":[117,132],"using":[118],"gradient":[119],"descent":[120],"boosting":[121],"more":[123],"accurate,":[124],"along":[125],"several":[126],"measures,":[127],"than":[128],"every":[129],"tested.":[133],"finding":[135],"could":[136,155],"explained":[138],"by":[139,158],"goodness-of-fit":[141],"method":[146],"available":[149],"data.":[150,164],"Alternatively,":[151],"performance":[154],"attenuated":[157],"necessary":[159],"limits":[160],"publicly-accessible":[162]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}