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dc.contributor.authorZerilli, Johnen
dc.contributor.authorKnott, Aen
dc.contributor.authorMaclaurin, Jen
dc.contributor.authorGavaghan, Cen
dc.date.accessioned2019-12-14T00:30:17Z
dc.date.available2019-12-14T00:30:17Z
dc.date.issued2019-12-01en
dc.identifier.issn0924-6495
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/299885
dc.description.abstractThe danger of human operators devolving responsibility to machines and failing to detect cases where they fail has been recognised for many years by industrial psychologists and engineers studying the human operators of complex machines. We call it “the control problem,” understood as the tendency of the human within a human-machine control loop to become complacent, over-reliant or unduly diffident when faced with the outputs of a reliable autonomous system. While the control problem has been investigated for some time, up to this point its manifestation in machine learning contexts has not received serious attention. This paper aims to fill that gap. We argue that, except in certain special circumstances, algorithmic decision tools should not be used in high-stakes or safety-critical decisions unless the systems concerned are significantly “better than human” in the relevant domain or subdomain of decision-making. More concretely, we recommend three strategies to address the control problem, the most promising of which involves a complementary (and potentially dynamic) coupling between highly proficient algorithmic tools and human agents working alongside one another. We also identify six key principles which all such human-machine systems should reflect in their design. These can serve as a framework both for assessing the viability of any such human-machine system as well as guiding the design and implementation of such systems generally.
dc.publisherSpringer Nature
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleAlgorithmic Decision-Making and the Control Problemen
dc.typeArticle
prism.endingPage578
prism.issueIdentifier4en
prism.publicationDate2019en
prism.publicationNameMinds and Machinesen
prism.startingPage555
prism.volume29en
dc.identifier.doi10.17863/CAM.46955
dcterms.dateAccepted2019-12-03en
rioxxterms.versionofrecord10.1007/s11023-019-09513-7en
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-12-01en
dc.contributor.orcidZerilli, John [0000-0002-7010-2278]
dc.identifier.eissn1572-8641
rioxxterms.typeJournal Article/Reviewen


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International