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

Author:

Buschek Daniel1,Eiband Malin2,Hussmann Heinrich2

Affiliation:

1. Department of Computer Science, University of Bayreuth, Bayreuth, Germany

2. LMU Munich, Munich, Germany

Abstract

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.

Funder

Bavarian State Ministry of Science and the Arts and coordinated by the Bavarian Research Institute for Digital Transformation

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Human-Computer Interaction

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. The Metacognitive Demands and Opportunities of Generative AI;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

2. Writer-Defined AI Personas for On-Demand Feedback Generation;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

3. A Conceptual Model Framework for XAI Requirement Elicitation of Application Domain System;IEEE Access;2023

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