TY - GEN
T1 - Exploring Mental Models for Explainable Artificial Intelligence
T2 - 4th International Conference on Artificial Intelligence in HCI, AI-HCI 2023, held as part of the 25th International Conference on Human-Computer Interaction, HCII 2023
AU - Sheridan, Helen
AU - Murphy, Emma
AU - O’Sullivan, Dympna
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Exploring end-users’ understanding of Artificial Intelligence (AI) systems’ behaviours and outputs is crucial in developing accessible Explainable Artificial Intelligence (XAI) solutions. Investigating mental models of AI systems is core in understanding and explaining the often opaque, complex, and unpredictable nature of AI. Researchers engage surveys, interviews, and observations for software systems, yielding useful evaluations. However, an evaluation gulf still exists, primarily around comprehending end-users’ understanding of AI systems. It has been argued that by exploring theories related to human decision-making examining the fields of psychology, philosophy, and human computer interaction (HCI) in a more people-centric rather than product or technology-centric approach can result in the creation of initial XAI solutions with great potential. Our work presents the results of a design thinking workshop with 14 cross-collaborative participants with backgrounds in philosophy, psychology, computer science, AI systems development and HCI. Participants undertook design thinking activities to ideate how AI system behaviours may be explained to end-users to bridge the explanation gulf of AI systems. We reflect on design thinking as a methodology for exploring end-users’ perceptions and mental models of AI systems with a view to creating effective, useful, and accessible XAI.
AB - Exploring end-users’ understanding of Artificial Intelligence (AI) systems’ behaviours and outputs is crucial in developing accessible Explainable Artificial Intelligence (XAI) solutions. Investigating mental models of AI systems is core in understanding and explaining the often opaque, complex, and unpredictable nature of AI. Researchers engage surveys, interviews, and observations for software systems, yielding useful evaluations. However, an evaluation gulf still exists, primarily around comprehending end-users’ understanding of AI systems. It has been argued that by exploring theories related to human decision-making examining the fields of psychology, philosophy, and human computer interaction (HCI) in a more people-centric rather than product or technology-centric approach can result in the creation of initial XAI solutions with great potential. Our work presents the results of a design thinking workshop with 14 cross-collaborative participants with backgrounds in philosophy, psychology, computer science, AI systems development and HCI. Participants undertook design thinking activities to ideate how AI system behaviours may be explained to end-users to bridge the explanation gulf of AI systems. We reflect on design thinking as a methodology for exploring end-users’ perceptions and mental models of AI systems with a view to creating effective, useful, and accessible XAI.
KW - Artificial Intelligence
KW - Design Thinking
KW - Explainable Artificial Intelligence
KW - Human Computer Interaction
UR - https://www.scopus.com/pages/publications/85171437635
U2 - 10.1007/978-3-031-35891-3_21
DO - 10.1007/978-3-031-35891-3_21
M3 - Conference contribution
AN - SCOPUS:85171437635
SN - 9783031358906
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 337
EP - 354
BT - Artificial Intelligence in HCI - 4th International Conference, AI-HCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Proceedings
A2 - Degen, Helmut
A2 - Ntoa, Stavroula
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 July 2023 through 28 July 2023
ER -