Algorithmic Recommendations and Human Discretion.
Human decision-makers frequently override the recommendations generated by predictive algorithms, but it is unclear whether these discretionary overrides add valuable private information or reintroduce human biases and mistakes. We develop new quasi-experimental tools to measure the impact of human...
| Publicado en: | Review of Economic Studies Vol. 93; no. 4; pp. 2250 - 2284 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Jul2026
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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=195161451&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 195161451 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00346527 REM jtl: Review of Economic Studies issn: 00346527 maglogo: N pubinfo: dt: Jul2026 vid: 93 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 195161451 10.1093/restud/rdaf084 ppf: 2250 ppct: 34 formats: tig: atl: Algorithmic Recommendations and Human Discretion. aug: au: Angelova, Victoria Dobbie, Will Yang, Crystal S affil: Harvard University, USA su: Judicial discretion Discretion Detention of persons Prediction algorithms Statistical bias Decision support systems Algorithms sug: subj: Judicial discretion Discretion Detention of persons Prediction algorithms Statistical bias Decision support systems Algorithms keyword: Bail decisions copyrightHolder:Review of Economic Studies Ltd copyrightYear:2026 Human discretion inLanguage:en publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/restud/rdaf084 Bail decisions copyrightHolder:Review of Economic Studies Ltd copyrightYear:2026 Human discretion inLanguage:en publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/restud/rdaf084 ab: Human decision-makers frequently override the recommendations generated by predictive algorithms, but it is unclear whether these discretionary overrides add valuable private information or reintroduce human biases and mistakes. We develop new quasi-experimental tools to measure the impact of human discretion over an algorithm on the accuracy of decisions, even when the outcome of interest is only selectively observed, in the context of bail decisions. We find that 90% of the judges in our setting underperform the algorithm when they make a discretionary override, with most making override decisions that are no better than random. Yet the remaining 10% of judges outperform the algorithm in terms of both accuracy and fairness when they make a discretionary override. We provide suggestive evidence on the behaviour underlying these differences in judge performance, showing that the high-performing judges are more likely to use relevant private information and are less likely to overreact to highly salient events compared to the low-performing judges. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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