Towards “Explorable” AI: Learning from ML Developers’ Sensemaking Practices

dc.contributor.authorWolf, Christine T.
dc.date.accessioned2021-05-18T10:05:04Z
dc.date.available2021-05-18T10:05:04Z
dc.date.issued2021
dc.description.abstractIn this note, we report on a qualitative design study in the field of machine learning (ML) and in particular on the sensemaking practices of ML developers as they interact with the interface of a novel adversarial AI method. This paper makes contributions to discourses on interpretable or explainable AI (XAI) systems through an empirical understanding of ML developers’ sensemaking practices. These findings make salient the concept of “explorability” as an alternative design metaphor for interactive AI systems – instead of a focus on explainability or interpretability as fixed qualities of AI systems, explorability focuses on emergent meanings and ways in which they might be enabled or constrained through practice.en
dc.identifier.doi10.18420/ecscw2021_n28
dc.identifier.pissn2510-2591
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4162
dc.language.isoen
dc.publisherEuropean Society for Socially Embedded Technologies (EUSSET)
dc.relation.ispartofProceedings of 19th European Conference on Computer-Supported Cooperative Work
dc.relation.ispartofseriesECSCW
dc.relation.ispartofseriesECSCW
dc.titleTowards “Explorable” AI: Learning from ML Developers’ Sensemaking Practicesen
dc.typeText/Conference Paper
gi.conference.date7-11 June 2021
gi.conference.locationZurich, Switzerland
gi.conference.sessiontitleNotes
mci.conference.reviewfull

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