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From "Explainable AI" to "Graspable AI"
Malmö University, Faculty of Culture and Society (KS), School of Arts and Communication (K3). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-1852-3937
Pennsylvania State University.
Machine Learning Visualization Lab Decisive Analytics Corporation, United States.
Aarhus University, Denmark.
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2021 (English)In: Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI ’21), New York: Association for Computing Machinery (ACM), 2021, article id 69Conference paper, Published paper (Refereed)
Abstract [en]

Since the advent of Artificial Intelligence (AI) and Machine Learning (ML), researchers have asked how intelligent computing systems could interact with and relate to their users and their surroundings, leading to debates around issues of biased AI systems, ML black-box, user trust, user’s perception of control over the system, and sys- tem’s transparency, to name a few. All of these issues are related to how humans interact with AI or ML systems, through an interface which uses different interaction modalities. Prior studies address these issues from a variety of perspectives, spanning from under- standing and framing the problems through ethics and Science and Technology Studies (STS) perspectives to finding effective technical solutions to the problems. But what is shared among almost all those efforts is an assumption that if systems can explain the how and why of their predictions, people will have a better perception of control and therefore will trust such systems more, and even can correct their shortcomings. This research field has been called Explainable AI (XAI). In this studio, we take stock on prior efforts in this area; however, we focus on using Tangible and Embodied Interaction (TEI) as an interaction modality for understanding ML. We note that the affordances of physical forms and their behaviors potentially can not only contribute to the explainability of ML sys- tems, but also can contribute to an open environment for criticism. This studio seeks to both critique explainable ML terminology and to map the opportunities that TEI can offer to the HCI for designing more sustainable, graspable and just intelligent systems.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2021. article id 69
Keywords [en]
Explainable AI, XAI, Machine Learning, Artificial Intelligence, Tan- gible Embodied Interaction, TEI, Interaction Design
National Category
Design Computer Systems
Research subject
Interaktionsdesign
Identifiers
URN: urn:nbn:se:mau:diva-38983DOI: 10.1145/3430524.3442704ISI: 001180182600069Scopus ID: 2-s2.0-85102059863ISBN: 978-1-4503-8213-7 (electronic)OAI: oai:DiVA.org:mau-38983DiVA, id: diva2:1514500
Conference
ACM International Conference on Tangible, Embedded and Embodied Interaction (TEI'21)
Funder
Knowledge FoundationAvailable from: 2021-01-05 Created: 2021-01-05 Last updated: 2024-05-28Bibliographically approved

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Ghajargar, MalihehCuartielles, David

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