Interactive Example-based Explanations to Improve Health Professionals' Onboarding with AI for Human-AI Collaborative Decision Making
Min Hun Lee, Renee Bao Xuan Ng, Silvana Xinyi Choo, and Shamala, Thilarajah

TL;DR
This paper introduces interactive example-based explanations to enhance health professionals' onboarding with AI, reducing overreliance on incorrect outputs and improving decision accuracy in human-AI collaboration.
Contribution
It presents a novel interactive explanation method that surfaces training set neighbors to improve AI onboarding for health professionals, addressing overreliance issues.
Findings
Improved reliance on AI during onboarding.
Higher accuracy in decision-making with example-based explanations.
Reduced incorrect decisions compared to feature-based explanations.
Abstract
A growing research explores the usage of AI explanations on user's decision phases for human-AI collaborative decision-making. However, previous studies found the issues of overreliance on `wrong' AI outputs. In this paper, we propose interactive example-based explanations to improve health professionals' onboarding with AI for their better reliance on AI during AI-assisted decision-making. We implemented an AI-based decision support system that utilizes a neural network to assess the quality of post-stroke survivors' exercises and interactive example-based explanations that systematically surface the nearest neighborhoods of a test/task sample from the training set of the AI model to assist users' onboarding with the AI model. To investigate the effect of interactive example-based explanations, we conducted a study with domain experts, health professionals to evaluate their performance…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Artificial Intelligence in Healthcare and Education · Impact of AI and Big Data on Business and Society
MethodsSparse Evolutionary Training
