Frictional AI. Designing Desirable Inefficiencies in Decision Support Systems for Knowledge Work

dc.contributor.authorNatali, Chiara
dc.date.accessioned2024-06-04T07:57:49Z
dc.date.available2024-06-04T07:57:49Z
dc.date.issued2024
dc.description.abstractMy research involves the conceptualization of ‘Frictional AI’ as a novel approach for enhancing cognitive engagement in Decision Support Systems (DSS) through intentional design inefficiencies. Through empirical studies and theoretical analysis, I explore the balance between human intuition and automation-induced enhancements to decision-making in medical diagnostics. With the introduction and assessment of four distinct frictional protocols (cautious, comparative, judicial, and adjunct), this design framework prioritizes the efficacy and integrity of human knowledge work, ensuring that professionals are engaged, critical thinkers, capable of counteracting automation bias and deskilling–even at a slight cost in efficiency and comfort.en
dc.identifier.doi10.48340/ecscw2024_dc07
dc.identifier.eissn2510-2591
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/5092
dc.language.isoen
dc.publisherEuropean Society for Socially Embedded Technologies (EUSSET)
dc.relation.ispartofProceedings of the 22nd European Conference on Computer-Supported Cooperative Work: The International Venue on Practice-centered Computing on the Design of Cooperation Technologies – Doctoral Colloquium Contributions
dc.relation.ispartofseriesReports of the European Society for Socially Embedded Technologies
dc.titleFrictional AI. Designing Desirable Inefficiencies in Decision Support Systems for Knowledge Worken
dc.typeText/Conference Paper
gi.conference.date17 June - 21 June, 2024
gi.conference.locationRimini, Italy
gi.conference.reviewfull
gi.conference.sessiontitleDoctoral Colloquium

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