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How to Support Users in Understanding Intelligent Systems? An Analysis and Conceptual Framework of User Questions Considering User Mindsets, Involvement and Knowledge Outcomes

Title data

Buschek, Daniel ; Eiband, Malin ; Hussmann, Heinrich:
How to Support Users in Understanding Intelligent Systems? An Analysis and Conceptual Framework of User Questions Considering User Mindsets, Involvement and Knowledge Outcomes.
In: ACM Transactions on Interactive Intelligent Systems. (February 2022) .
ISSN 2160-6455
DOI: https://doi.org/10.1145/3519264

Official URL: Volltext

Project information

Project title:
Project's official titleProject's id
AI Tools - Continuous Interaction with Computational Intelligence ToolsNo information

Abstract in another language

The opaque nature of many intelligent systems violates established usability principles and thus presents a challenge for human-computer interaction. Research in the field therefore highlights the need for transparency, scrutability, intelligibility, interpretability and explainability, among others. While all of these terms carry a vision of supporting users in understanding intelligent systems, the underlying notions and assumptions about users and their interaction with the system often remain unclear. We review the literature in HCI through the lens of implied user questions to synthesise a conceptual framework integrating user mindsets, user involvement, and knowledge outcomes to reveal, differentiate and classify current notions in prior work. This framework aims to resolve conceptual ambiguity in the field and enables researchers to clarify their assumptions and become aware of those made in prior work. We further discuss related aspects such as stakeholders and trust, and also provide material to apply our framework in practice (e.g. ideation / design sessions). We thus hope to advance and structure the dialogue on supporting users in understanding intelligent systems.

Further data

Item Type: Article in a journal
Refereed: Yes
Keywords: scrutability; interpretability; interactive machine learning; transparency; end-user debugging; Review; accountability; intelligibility; explainability; intelligent systems
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 15 Jun 2022 09:17
Last Modified: 15 Jun 2022 09:17
URI: https://eref.uni-bayreuth.de/id/eprint/70109