abstract

Designing and evaluating explainable AI for non-AI experts: challenges and opportunities

Authors:

Maxwell Szymanski,

Katrien Verbert,

Vero Vanden AbeeleAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 735 - 736

https://doi.org/10.1145/3523227.3547427

Published: 13 September 2022 Publication History

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Abstract

Artificial intelligence (AI) has seen a steady increase in use in the health and medical field, where it is used by lay users and health experts alike. However, these AI systems often lack transparency regarding the inputs and decision making process (often called black boxes), which in turn can be detrimental to the user’s satisfaction and trust towards these systems. Explainable AI (XAI) aims to overcome this problem by opening up certain aspects of the black box, and has proven to be a successful means of increasing trust, transparency and even system effectiveness. However, for certain groups (i.e. lay users in health), explanation methods and evaluation metrics still remain underexplored. In this paper, we will outline our research regarding designing and evaluating explanations for health recommendations for lay users and domain experts, as well as list a few takeaways we were already able to find in our initial studies.

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Mi JJiang XLuo LGao Y(2024)Toward explainable artificial intelligenceNeurocomputing10.1016/j.neucom.2023.126919563:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.126919
Wang XYin M(2023)Watch Out for Updates: Understanding the Effects of Model Explanation Updates in AI-Assisted Decision MakingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581366(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581366

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Designing and evaluating explainable AI for non-AI experts: challenges and opportunities

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Published In

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 13 September 2022

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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Mi JJiang XLuo LGao Y(2024)Toward explainable artificial intelligenceNeurocomputing10.1016/j.neucom.2023.126919563:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.126919
Wang XYin M(2023)Watch Out for Updates: Understanding the Effects of Model Explanation Updates in AI-Assisted Decision MakingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581366(1-19)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581366

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An Argumentative Framework for Generating Explainable Group Recommendations

Methods and standards for research on explainable artificial intelligence: Lessons from intelligent tutoring systems

Counterfactual Explainable Recommendation

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