{"id":"https://openalex.org/W7204896573","doi":"https://doi.org/10.5281/zenodo.21904686","title":"Gender Inequality in the Artificial Intelligence Workforce: Longitudinal Global Evidence of Pipeline Leakage","display_name":"Gender Inequality in the Artificial Intelligence Workforce: Longitudinal Global Evidence of Pipeline Leakage","publication_year":2026,"publication_date":"2026-09-01","ids":{"openalex":"https://openalex.org/W7204896573","doi":"https://doi.org/10.5281/zenodo.21904686"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.21904686","doi":"https://doi.org/10.5281/zenodo.21904686","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.21904686","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"parent_works":[],"lineage":["https://openalex.org/W7204896573"],"child_works_count":0,"type":"dataset","is_traditional":false,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.21904686","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121336181","display_name":"Priscillia Adaku Kama","orcid":"https://orcid.org/0009-0008-1557-9242","observed_orcids":["https://orcid.org/0009-0008-1557-9242"]},"institutions":[{"id":"https://openalex.org/I73621328","display_name":"Girne American University","ror":"https://ror.org/026b8w395","country_code":"CY","type":"education","lineage":["https://openalex.org/I73621328"]}],"countries":["CY"],"is_corresponding":false,"raw_author_name":"KAMA, PRISCILLIA ADAKU","raw_affiliation_strings":["Girne American University"],"raw_orcid":"https://orcid.org/0009-0008-1557-9242","affiliations":[{"raw_affiliation_string":"Girne American University","institution_ids":["https://openalex.org/I73621328"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044519326","display_name":"Alper G\u00dcLBAY","orcid":"https://orcid.org/0000-0003-1456-7481","observed_orcids":["https://orcid.org/0000-0003-1456-7481"]},"institutions":[{"id":"https://openalex.org/I73621328","display_name":"Girne American University","ror":"https://ror.org/026b8w395","country_code":"CY","type":"education","lineage":["https://openalex.org/I73621328"]}],"countries":["CY"],"is_corresponding":false,"raw_author_name":"G\u00dcLBAY, ALPER","raw_affiliation_strings":["Girne American University"],"raw_orcid":"https://orcid.org/0000-0003-1456-7481","affiliations":[{"raw_affiliation_string":"Girne American University","institution_ids":["https://openalex.org/I73621328"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I73621328"],"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.5351999998092651,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.5351999998092651,"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/T12515","display_name":"Gender and Technology in Education","score":0.26100000739097595,"subfield":{"id":"https://openalex.org/subfields/3318","display_name":"Gender Studies"},"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/T10726","display_name":"Career Development and Diversity","score":0.14900000393390656,"subfield":{"id":"https://openalex.org/subfields/3202","display_name":"Applied Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gender-inequality","display_name":"gender inequality","score":0.9980000257492065},{"id":"https://openalex.org/keywords/artificial-intelligence-workforce","display_name":"artificial intelligence workforce","score":0.9869999885559082},{"id":"https://openalex.org/keywords/data-science-workforce","display_name":"data science workforce","score":0.9729999899864197},{"id":"https://openalex.org/keywords/pipeline-leakage","display_name":"pipe leakage","score":0.8650000095367432},{"id":"https://openalex.org/keywords/responsible-innovation","display_name":"responsible innovation","score":0.7979999780654907},{"id":"https://openalex.org/keywords/inclusion","display_name":"inclusion","score":0.5519999861717224},{"id":"https://openalex.org/keywords/digital-inequality","display_name":"digital inequality","score":0.6380000114440918},{"id":"https://openalex.org/keywords/social-sustainability","display_name":"social sustainability","score":0.8299999833106995},{"id":"https://openalex.org/keywords/sustainable-development-goals","display_name":"Sustainable Development Goals","score":0.8740000128746033},{"id":"https://openalex.org/keywords/compensation","display_name":"compensation","score":0.4020000100135803},{"id":"https://openalex.org/keywords/geographical-representation","display_name":"geographical representation","score":0.6510000228881836}],"concepts":[{"id":"https://openalex.org/C56666940","wikidata":"https://www.wikidata.org/wiki/Q788790","display_name":"Documentation","level":2,"score":0.553600013256073},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.548799991607666},{"id":"https://openalex.org/C2778139618","wikidata":"https://www.wikidata.org/wiki/Q13440398","display_name":"Workforce","level":2,"score":0.4625999927520752},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46239998936653137},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4499000012874603},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.4147999882698059},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.39100000262260437},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3695000112056732},{"id":"https://openalex.org/C2779968149","wikidata":"https://www.wikidata.org/wiki/Q8034851","display_name":"Workforce development","level":3,"score":0.35740000009536743},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3138999938964844},{"id":"https://openalex.org/C12590798","wikidata":"https://www.wikidata.org/wiki/Q3933199","display_name":"Replication (statistics)","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C552854447","wikidata":"https://www.wikidata.org/wiki/Q131201","display_name":"Sustainable development","level":2,"score":0.30720001459121704},{"id":"https://openalex.org/C86844869","wikidata":"https://www.wikidata.org/wiki/Q2798820","display_name":"Hacker","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C196879817","wikidata":"https://www.wikidata.org/wiki/Q872685","display_name":"Data governance","level":4,"score":0.3034999966621399},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.2915000021457672},{"id":"https://openalex.org/C171730128","wikidata":"https://www.wikidata.org/wiki/Q5227290","display_name":"Data file","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C2780233690","wikidata":"https://www.wikidata.org/wiki/Q535347","display_name":"Transparency (behavior)","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C2777895361","wikidata":"https://www.wikidata.org/wiki/Q1758614","display_name":"Longitudinal study","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C45555294","wikidata":"https://www.wikidata.org/wiki/Q28113351","display_name":"Inequality","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C2776007630","wikidata":"https://www.wikidata.org/wiki/Q2798912","display_name":"Accountability","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.26840001344680786},{"id":"https://openalex.org/C39389867","wikidata":"https://www.wikidata.org/wiki/Q380767","display_name":"Corporate governance","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C173655357","wikidata":"https://www.wikidata.org/wiki/Q244752","display_name":"Digital divide","level":3,"score":0.25110000371932983}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.5281/zenodo.21904686","doi":"https://doi.org/10.5281/zenodo.21904686","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.21904686","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},{"id":"doi:10.5281/zenodo.21904687","doi":"https://doi.org/10.5281/zenodo.21904687","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.21904687","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},{"id":"doi:10.5281/zenodo.22808988","doi":"https://doi.org/10.5281/zenodo.22808988","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.22808988","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"}],"best_oa_location":{"id":"doi:10.5281/zenodo.21904686","doi":"https://doi.org/10.5281/zenodo.21904686","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.21904686","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Dataset"},"sustainable_development_goals":[{"display_name":"Gender equality","id":"https://metadata.un.org/sdg/5","score":1.0},{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":1.0},{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.6452000141143799}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4415010864","https://openalex.org/W4407604171","https://openalex.org/W7214888114","https://openalex.org/W4386222135","https://openalex.org/W7155093544","https://openalex.org/W7125433183","https://openalex.org/W7166591076","https://openalex.org/W7214718927","https://openalex.org/W4388081662","https://openalex.org/W7203945418"],"abstract_inverted_index":{"Description":[0],"Replication":[1],"Package:":[2],"Responsible":[3],"Innovation":[4,23],"and":[5,13,24,32,45,58,85,93,153,180,183,204,213,215,255,272,290,296],"Gender":[6,25],"Inequality":[7,26],"in":[8,27,40,219,230],"Artificial":[9,28],"Intelligence:":[10,29],"Workforce":[11,30],"Inclusion":[12,31],"Social":[14,33],"Sustainability":[15],"This":[16],"replication":[17,160],"package":[18,161,283],"accompanies":[19],"the":[20,41,54,107,114,143,154,170,188,198,225,231,252,264,293],"study":[21,36,97,140,235],"\u201cResponsible":[22],"Sustainability.\u201d":[34],"The":[35,71,96,139,159,191,234,243,268,282],"examines":[37,73],"gender":[38,74,129],"inequality":[39],"artificial":[42],"intelligence":[43],"(AI)":[44],"data":[46],"science":[47],"workforce":[48,77,137],"using":[49],"four":[50,244],"consecutive":[51],"waves":[52,246],"of":[53,136,145,251,263,292],"Kaggle":[55,201,253],"Machine":[56,202],"Learning":[57,203],"Data":[59,205],"Science":[60,206],"Survey":[61,207],"(2018\u20132021),":[62],"comprising":[63],"87,945":[64],"respondents":[65],"from":[66,197],"more":[67],"than":[68],"50":[69],"countries.":[70],"analysis":[72,172],"differences":[75],"across":[76,133],"participation,":[78],"age,":[79],"educational":[80],"attainment,":[81],"occupational":[82],"roles,":[83],"coding":[84],"machine":[86],"learning":[87],"experience,":[88],"annual":[89],"compensation,":[90],"industry":[91],"sectors,":[92],"geographic":[94],"representation.":[95],"is":[98,120,284],"guided":[99],"by":[100,113],"responsible":[101,109],"innovation":[102,110],"theory,":[103],"with":[104],"inclusion":[105],"as":[106,122,260],"principal":[108],"dimension":[111],"implicated":[112],"empirical":[115,171],"findings.":[116,298],"STEM":[117],"Pipeline":[118],"Theory":[119],"used":[121],"an":[123],"explanatory":[124],"mechanism":[125],"for":[126,148,209,223],"understanding":[127],"how":[128],"disparities":[130],"may":[131],"accumulate":[132],"successive":[134],"stages":[135],"participation.":[138],"also":[141],"considers":[142],"implications":[144],"these":[146],"patterns":[147],"social":[149],"sustainability,":[150],"digital":[151],"inequality,":[152],"Sustainable":[155],"Development":[156],"Goals":[157],"(SDGs).":[158],"contains":[162],"nine":[163],"Python":[164],"3.9":[165],"analytical":[166,294],"scripts,":[167],"figures":[168],"supporting":[169],"(Figures":[173],"1\u20137),":[174],"a":[175,184],"README":[176],"file":[177,186],"providing":[178],"documentation":[179],"reproduction":[181],"instructions,":[182],"requirements":[185],"listing":[187],"software":[189],"dependencies.":[190],"underlying":[192],"survey":[193,241,245],"datasets":[194,227],"were":[195],"obtained":[196],"publicly":[199,237],"available":[200],"repositories":[208],"2018,":[210],"2019,":[211],"2020,":[212],"2021":[214],"are":[216,228,247,270],"not":[217,257,274],"redistributed":[218],"this":[220],"package.":[221],"Instructions":[222],"accessing":[224],"original":[226],"provided":[229,285],"accompanying":[232],"documentation.":[233],"uses":[236],"available,":[238],"anonymised":[239],"secondary":[240],"data.":[242],"voluntary,":[248],"non-probability":[249],"samples":[250,262],"community":[254],"should":[256],"be":[258],"interpreted":[259],"representative":[261],"global":[265],"AI":[266],"workforce.":[267],"analyses":[269],"descriptive":[271],"do":[273],"involve":[275],"individual-level":[276],"longitudinal":[277],"matching":[278],"or":[279],"causal":[280],"inference.":[281],"to":[286],"support":[287],"transparency,":[288],"reproducibility,":[289],"verification":[291],"procedures":[295],"reported":[297]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-09-02T00:00:00"}
