{"id":"https://openalex.org/W4394789495","doi":"https://doi.org/10.1093/pubmed/fdae052","title":"What\u2019s artificial intelligence (AI) got to do with it\u2014inequality and public health?","display_name":"What\u2019s artificial intelligence (AI) got to do with it\u2014inequality and public health?","publication_year":2024,"publication_date":"2024-04-11","ids":{"openalex":"https://openalex.org/W4394789495","doi":"https://doi.org/10.1093/pubmed/fdae052","pmid":"https://pubmed.ncbi.nlm.nih.gov/38609188"},"language":"en","primary_location":{"id":"doi:10.1093/pubmed/fdae052","doi":"https://doi.org/10.1093/pubmed/fdae052","is_oa":true,"landing_page_url":"https://doi.org/10.1093/pubmed/fdae052","pdf_url":"https://academic.oup.com/jpubhealth/advance-article-pdf/doi/10.1093/pubmed/fdae052/57216194/fdae052.pdf","source":{"id":"https://openalex.org/S27214173","display_name":"Journal of Public Health","issn_l":"0943-1853","issn":["0943-1853","1613-2238","1741-3842","1741-3850","2198-1833"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","dongbi-d","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-40","poland-70","russia-white-list-1","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Public Health","raw_type":"journal-article"},"volume":null,"type":"editorial","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://academic.oup.com/jpubhealth/advance-article-pdf/doi/10.1093/pubmed/fdae052/57216194/fdae052.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109279754","display_name":"Premila Webster","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Premila Webster","raw_affiliation_strings":["Somerville College, University of Oxford"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Somerville College, University of Oxford","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019155992","display_name":"Keith R Neal","orcid":"https://orcid.org/0000-0002-5452-4016","observed_orcids":["https://orcid.org/0000-0002-5452-4016"]},"institutions":[{"id":"https://openalex.org/I142263535","display_name":"University of Nottingham","ror":"https://ror.org/01ee9ar58","country_code":"GB","type":"education","lineage":["https://openalex.org/I142263535"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Keith R Neal","raw_affiliation_strings":["Epidemiology, University of Nottingham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Epidemiology, University of Nottingham","institution_ids":["https://openalex.org/I142263535"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5109279754"],"corresponding_institution_ids":["https://openalex.org/I40120149"],"apc_list":{"value":2490,"currency":"GBP","value_usd":3690},"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"46","issue":"2","first_page":"207","last_page":"208"},"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":0.9854000210762024,"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":0.9854000210762024,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.013799999840557575,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"artificial intelligence","score":0.9779999852180481},{"id":"https://openalex.org/keywords/inequality","display_name":"inequality","score":0.7099999785423279},{"id":"https://openalex.org/keywords/public-health","display_name":"public health","score":0.9580000042915344},{"id":"https://openalex.org/keywords/algorithmic-bias","display_name":"algorithmic bias","score":0.8550000190734863},{"id":"https://openalex.org/keywords/fairness-accountability-and-transparency","display_name":"fairness, accountability, and transparency","score":0.7509999871253967},{"id":"https://openalex.org/keywords/machine-learning","display_name":"machine learning","score":0.5339999794960022},{"id":"https://openalex.org/keywords/health-data","display_name":"health data","score":0.33000001311302185},{"id":"https://openalex.org/keywords/digital-divide","display_name":"digital divide","score":0.5559999942779541}],"concepts":[{"id":"https://openalex.org/C138816342","wikidata":"https://www.wikidata.org/wiki/Q189603","display_name":"Public health","level":2,"score":0.6297174692153931},{"id":"https://openalex.org/C45555294","wikidata":"https://www.wikidata.org/wiki/Q28113351","display_name":"Inequality","level":2,"score":0.5863183736801147},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.40945369005203247},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.35004985332489014},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.31747856736183167},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13809189200401306},{"id":"https://openalex.org/C159110408","wikidata":"https://www.wikidata.org/wiki/Q121176","display_name":"Nursing","level":1,"score":0.10283616185188293},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D001185","descriptor_name":"Artificial Intelligence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011634","descriptor_name":"Public Health","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1093/pubmed/fdae052","doi":"https://doi.org/10.1093/pubmed/fdae052","is_oa":true,"landing_page_url":"https://doi.org/10.1093/pubmed/fdae052","pdf_url":"https://academic.oup.com/jpubhealth/advance-article-pdf/doi/10.1093/pubmed/fdae052/57216194/fdae052.pdf","source":{"id":"https://openalex.org/S27214173","display_name":"Journal of Public Health","issn_l":"0943-1853","issn":["0943-1853","1613-2238","1741-3842","1741-3850","2198-1833"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","dongbi-d","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-40","poland-70","russia-white-list-1","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Public Health","raw_type":"journal-article"},{"id":"pmid:38609188","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38609188","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of public health (Oxford, England)","raw_type":"Editorial"}],"best_oa_location":{"id":"doi:10.1093/pubmed/fdae052","doi":"https://doi.org/10.1093/pubmed/fdae052","is_oa":true,"landing_page_url":"https://doi.org/10.1093/pubmed/fdae052","pdf_url":"https://academic.oup.com/jpubhealth/advance-article-pdf/doi/10.1093/pubmed/fdae052/57216194/fdae052.pdf","source":{"id":"https://openalex.org/S27214173","display_name":"Journal of Public Health","issn_l":"0943-1853","issn":["0943-1853","1613-2238","1741-3842","1741-3850","2198-1833"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-b","dongbi-d","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-40","poland-70","russia-white-list-1","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Public Health","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7578999996185303,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"sustainable_development_goals_aurora":[{"score":0.4000000059604645,"display_name":"Reduced inequalities","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/W4394789495.pdf","grobid_xml":"https://content.openalex.org/works/W4394789495.grobid-xml"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W1557463023","https://openalex.org/W2750144484","https://openalex.org/W3137761476","https://openalex.org/W3138994570","https://openalex.org/W4210406706","https://openalex.org/W4226016139","https://openalex.org/W4309702579","https://openalex.org/W4387956898"],"related_works":["https://openalex.org/W3005183248","https://openalex.org/W4304194092","https://openalex.org/W4252485616","https://openalex.org/W2788169366","https://openalex.org/W2791443286","https://openalex.org/W3209754092","https://openalex.org/W4379508531","https://openalex.org/W6941247196","https://openalex.org/W4247403152","https://openalex.org/W4415648946"],"abstract_inverted_index":{"\u2018We":[0],"must":[1],"work":[2],"together":[3,554],"for":[4,82,95,305,411,420,464,670,678,703,734,832],"AI":[5,24,43,126,136,149,153,229,268,282,297,328,338,356,409,551,655,752,782,831,849,882,898,908,927],"that":[6,15,104,237,360,429,508,547,562,577,607,616,654,715],"bridges":[7],"social,":[8],"digital,":[9],"and":[10,36,44,51,93,143,202,216,246,259,286,294,340,369,377,388,417,447,482,485,502,591,594,611,632,640,648,739,751,798,843,848,867,895,904,917,922],"economic":[11,465],"divides,":[12],"not":[13,147],"one":[14,70],"pushes":[16],"us":[17],"further":[18],"apart.\u20191":[19],"In":[20,528],"his":[21,690],"article":[22,530],"\u2018Getting":[23],"Right\u20192":[25],"James":[26],"Maniyka":[27],"writes:":[28],"\u2018While":[29],"the":[30,111,117,182,195,198,292,325,333,347,375,380,397,421,427,440,486,495,506,511,520,575,694,708,760,785,893,902,924,935],"headlines":[31],"tend":[32],"to":[33,41,75,98,167,180,213,250,270,313,349,355,365,468,493,513,518,534,552,559,567,604,623,636,644,663,720,775,780,788,793,822,853,875,889,891,920,928,933],"feature":[34],"results":[35,587],"demonstrations":[37],"of":[38,110,113,119,124,185,189,197,225,266,291,296,308,322,327,332,379,399,435,442,451,475,488,505,522,550,574,588,839,857,897,907,915,926,937],"a":[39,100,155,162,223,303,306,392,538,620,731,790,795,808,816,854,887,913],"future":[40,596,646],"come,":[42],"its":[45,645],"associated":[46],"technologies":[47,339,492],"are":[48,231,383,432,504,573,608,642,673,726,754,805],"already":[49,727],"here":[50,764],"pervade":[52],"our":[53],"daily":[54],"lives":[55],"more":[56,68,809],"than":[57,69],"many":[58],"realize.":[59],"Examples":[60],"include":[61],"recommendation":[62],"systems,":[63,89,92],"search,":[64],"language":[65],"translators\u2013now":[66],"covering":[67],"hundred":[71],"languages\u2013facial":[72],"recognition,":[73],"speech":[74],"text":[76],"(and":[77],"back),":[78],"digital":[79],"assistants,":[80],"chatbots":[81],"customer":[83],"service,":[84],"fraud":[85],"detection,":[86],"decision":[87],"support":[88],"energy":[90],"management":[91],"tools":[94],"scientific":[96],"research,":[97],"name":[99],"few\u2019.":[101],"He":[102],"continues":[103],"in":[105,194,252,396,407,525,582,634,658,689,765,800,851,910,940],"2006":[106],"Nick":[107],"Bostrom,3":[108],"director":[109],"Future":[112],"Humanity":[114],"Institute":[115],"at":[116,344,693,806],"University":[118],"Oxford":[120],"noted,":[121],"\u201ca":[122],"lot":[123],"cutting-edge":[125],"has":[127,390,425,477,510,619,656,700,712,886],"filtered":[128],"into":[129],"general":[130],"applications,":[131],"often":[132],"without":[133],"being":[134],"called":[135,160,227],"because":[137],"once":[138],"something":[139],"becomes":[140],"useful":[141],"enough":[142,145],"common":[144],"it\u2019s":[146],"labelled":[148],"anymore\u201d.":[150],"The":[151,264,829],"term":[152],"is":[154,191,275,283,300,318,336,363,455,458,462,651,717,743,787],"complex":[156,183],"one.":[157],"Marvin":[158],"Minsky4":[159],"it":[161,190,299,638,716],"\u2018suitcase":[163],"word\u2019":[164],"which":[165,362],"according":[166],"him":[168],"\u2018is":[169],"packed":[170],"variously,":[171],"depending":[172],"on":[173,324,329,439,480,818],"who":[174,454,457,461],"you":[175,288],"ask\u2019.":[176],"I":[177],"don\u2019t":[178],"claim":[179],"understand":[181],"world":[184],"AI.":[186],"My":[187],"understanding":[188],"as":[192,277,302,820,825,827],"outlined":[193],"introduction":[196],"UNESCO":[199],"Artificial":[200],"Intelligence":[201],"Gender":[203],"Equality":[204],"report5\u2014\u2018Simply":[205],"put,":[206],"artificial":[207,489,532],"intelligence":[208,490,533],"(AI)":[209,491],"involves":[210],"using":[211,222,233,881],"computers":[212],"classify,":[214],"analyze,":[215],"draw":[217,553],"predictions":[218],"from":[219,256,310,661,676],"data":[220,258,404],"sets,":[221],"set":[224],"rules":[226],"algorithms.":[228],"algorithms":[230],"trained":[232],"large":[234],"datasets":[235,556],"so":[236],"they":[238,382],"can":[239,579],"identify":[240,789],"patterns,":[241],"make":[242,367],"predictions,":[243],"recommend":[244],"actions,":[245],"figure":[247],"out":[248],"what":[249],"do":[251],"unfamiliar":[253],"situations,":[254],"learning":[255],"new":[257,629],"thus":[260],"improving":[261,595],"over":[262,346],"time.":[263],"ability":[265,517,549,932],"an":[267,319,583],"system":[269],"improve":[271,514,535],"automatically":[272],"through":[273],"experience":[274],"known":[276],"Machine":[278],"Learning":[279],"(ML)\u2019.":[280],"However,":[281,667],"all":[284,523,526,628,938,941],"pervading":[285],"if":[287],"believe":[289],"some":[290,652],"websites":[293],"CEOs":[295],"companies6,7":[298],"offered":[301],"panacea":[304],"range":[307,856,914],"issues":[309,334,748],"ending":[311],"poverty":[312],"reversing":[314],"climate":[315,750],"change.":[316],"There":[317,650],"increasing":[320],"body":[321],"research":[323,394],"impact":[326,474],"inequality.":[330,722],"One":[331],"debated":[335],"whether":[337],"their":[341,529],"applications":[342,783],"may,":[343],"least":[345],"short":[348],"medium":[350],"term,":[351],"increase":[352],"inequality":[353],"due":[354],"automation.8,9":[357],"Research":[358,424],"suggests":[359],"AI,":[361],"expected":[364],"be":[366,560,565,580,605,813],"objective":[368],"unbiased":[370],"judgments":[371],"ends":[372],"up":[373],"reproducing":[374],"biases":[376],"inequalities":[378],"societies":[381],"\u2018trained\u2019":[384],"on.":[385],"\u2018Fairness,":[386],"accountability":[387],"transparency\u2019":[389],"become":[391],"major":[393],"agenda":[395],"field":[398],"machine":[400],"learning.10":[401],"Inadequate":[402],"training":[403],"have":[405,770],"resulted":[406],"inaccurate":[408],"models":[410],"disease":[412],"risk":[413],"assessment,":[414],"prognostic":[415],"prediction,":[416],"medication":[418],"usage":[419],"data-disadvantaged":[422],"populations.11\u201314":[423],"documented":[426],"ways":[428],"\u2018automated":[430],"decisions":[431],"depriving":[433],"people":[434,524,870,939],"government":[436],"benefits,":[437],"discriminating":[438],"basis":[441],"sex,":[443],"skin":[444],"color,":[445],"age":[446],"numerous":[448],"other":[449,781,858],"forms":[450],"difference,":[452],"choosing":[453],"surveilled,":[456],"imprisoned,":[459],"or":[460,680],"targeted":[463,665],"exploitation\u2019.14":[466],"According":[467],"David":[469,541],"Leslie":[470],"et":[471,544],"al13":[472],"\u2018The":[473],"covid-19":[476],"fallen":[478],"disproportionately":[479],"disadvantaged":[481],"vulnerable":[483,803],"communities,":[484],"use":[487,637],"combat":[494],"pandemic":[496],"risks":[497],"compounding":[498],"these":[499,599],"inequities\u2019.":[500],"Fisher":[501],"Rosella15":[503],"opinion":[507,576],"\u2018AI":[509,618],"potential":[512,702,925],"public":[515,536,659,671,840,930],"health\u2019s":[516,931],"promote":[519,934],"health":[521,660,672,681,841,878,936],"communities\u2019.":[527],"\u2018Using":[531],"health:":[537],"narrative":[539],"review\u2019":[540],"B.":[542],"Olawade":[543],"al16":[545],"contend":[546],"\u2018the":[548,668],"various":[555],"allows":[557],"insights":[558,600],"made":[561],"would":[563],"otherwise":[564],"difficult":[566],"develop":[568],"with":[569,586,730,777,815,845,871,912],"traditional":[570],"methods\u2019.":[571],"They":[572,613],"\u2018Algorithms":[578],"employed":[581],"iterative":[584],"manner,":[585],"policies":[589,603],"monitored,":[590],"subsequently":[592],"informing":[593],"policies.":[597],"Leveraging":[598],"will":[601],"allow":[602],"developed":[606],"better-targeted,":[609],"impactful":[610],"timely\u2019.":[612],"also":[614],"warn":[615],"though":[617],"great":[621],"deal":[622,776],"offer":[624],"policymakers":[625],"but,":[626],"like":[627],"technologies,":[630],"trust":[631],"education":[633],"how":[635],"effectively":[639],"responsibly":[641],"critical":[643],"uptake":[647],"usefulness\u2019.":[649],"evidence":[653],"uses":[657],"surveillance":[662],"developing":[664],"policy.":[666],"considerations":[669],"distinctly":[674],"different":[675],"those":[677],"clinical":[679],"care":[682],"systems\u2019.15":[683],"To":[684],"quote":[685],"UN\u2019s":[686],"Ant\u00f3nio":[687],"Guterres":[688],"special":[691],"address":[692],"World":[695],"Economic":[696],"Forum17":[697],"\u2018This":[698],"technology":[699],"enormous":[701],"sustainable":[704],"development":[705],"\u2013":[706,749,753],"but":[707],"International":[709],"Monetary":[710],"Fund":[711],"just":[713],"warned":[714],"very":[718],"likely":[719],"worsen":[721],"Powerful":[723],"tech":[724],"companies":[725],"pursuing":[728],"profits":[729],"reckless":[732],"disregard":[733],"human":[735],"rights,":[736],"personal":[737],"privacy,":[738],"social":[740],"impact.":[741],"This":[742],"no":[744,771],"secret.":[745],"These":[746],"two":[747],"exhaustively":[755],"discussed":[756],"by":[757,759,762],"governments,":[758],"media,":[761],"leaders":[763],"Davos.":[766],"And":[767],"yet,":[768],"we":[769],"effective":[772],"global":[773],"strategy":[774],"either\u2019.":[778],"\u2018Contrary":[779],"where":[784,802],"goal":[786],"single":[791,796],"model":[792],"beat":[794],"metric":[797],"benchmark,":[799],"situations":[801],"populations":[804],"risk,":[807],"sensitive":[810],"approach":[811],"should":[812],"taken":[814],"focus":[817],"outliers":[819],"metrics":[821],"assess":[823],"failure":[824],"well":[826],"success\u2019.11":[828],"CIFAR":[830],"Public":[833,884],"Health":[834,885],"Equity":[835],"report18":[836],"recommends":[837],"\u2018collaboration":[838],"professionals":[842],"researchers":[844],"computer":[846],"science":[847],"researchers,":[850],"addition":[852],"wide":[855],"groups":[859],"(e.g.,":[860],"sociologists,":[861],"political":[862],"scientists,":[863,869],"engineers,":[864],"civil":[865,918],"society":[866,919],"citizen":[868],"lived":[872],"experience,":[873],"policymakers)":[874],"help":[876],"ensure":[877],"equity":[879],"when":[880],"technologies\u2019.":[883],"role":[888],"play":[890],"garner":[892],"possibilities":[894],"benefits":[896],"while":[899],"carefully":[900],"considering":[901],"challenges":[903],"unintended":[905],"consequences":[906],"working":[909],"collaboration":[911],"experts":[916],"explore":[921],"exploit":[923],"\u2018improve":[929],"communities\u2019.15":[942]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":11}],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2024-04-14T00:00:00"}
