Using Artificial Intelligence to classify Jobseekers: The Accuracy-Equity Trade-off.
Artificial intelligence (AI) is increasingly popular in the public sector to improve the cost-efficiency of service delivery. One example is AI-based profiling models in public employment services (PES), which predict a jobseeker's probability of finding work and are used to segment jobseekers in gr...
| Publicado en: | Journal of Social Policy Vol. 50; no. 2; pp. 367 - 386 |
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| Autores principales: | , |
| Formato: | Artículo |
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Cambridge University Press
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=149012232&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 149012232 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00472794 JSP jtl: Journal of Social Policy issn: 00472794 maglogo: N pubinfo: dt: Apr2021 vid: 50 iid: 2 pid: 15979 pub: Cambridge University Press artinfo: ui: 149012232 10.1017/S0047279420000203 ppf: 367 ppct: 19 formats: tig: atl: Using Artificial Intelligence to classify Jobseekers: The Accuracy-Equity Trade-off. aug: au: DESIERE, SAM STRUYVEN, LUDO affil: HIVA, KU Leuven, 3000 Leuven , Belgium su: Belgium Prevention of employment discrimination Unemployment & psychology Immigrants Communicative competence Artificial intelligence Public sector Labor market Employment agencies Random forest algorithms Employee selection Employment portfolios Descriptive statistics Statistical models Probability theory sug: subj: Prevention of employment discrimination Unemployment & psychology Immigrants Communicative competence Artificial intelligence Public sector Labor market Belgium Federal labour and employment services Provincial labour and employment services Employment placement agencies and executive search services Employment Placement Agencies Employment agencies Random forest algorithms Employee selection Employment portfolios Descriptive statistics Statistical models Probability theory keyword: artificial intelligence profiling public employment services statistical discrimination VDAB artificial intelligence profiling public employment services statistical discrimination VDAB ab: Artificial intelligence (AI) is increasingly popular in the public sector to improve the cost-efficiency of service delivery. One example is AI-based profiling models in public employment services (PES), which predict a jobseeker's probability of finding work and are used to segment jobseekers in groups. Profiling models hold the potential to improve identification of jobseekers at-risk of becoming long-term unemployed, but also induce discrimination. Using a recently developed AI-based profiling model of the Flemish PES, we assess to what extent AI-based profiling 'discriminates' against jobseekers of foreign origin compared to traditional rule-based profiling approaches. At a maximum level of accuracy, jobseekers of foreign origin who ultimately find a job are 2.6 times more likely to be misclassified as 'high-risk' jobseekers. We argue that it is critical that policymakers and caseworkers understand the inherent trade-offs of profiling models, and consider the limitations when integrating these models in daily operations. We develop a graphical tool to visualize the accuracy-equity trade-off in order to facilitate policy discussions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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