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Interactive Example-based Explanations to Improve Health Professionals' Onboarding with AI for Human-AI Collaborative Decision Making

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arxiv 2409.15814 v1 pith:Z2GIROBN submitted 2024-09-24 cs.HC cs.AIcs.LG

Min Hun Lee , Renee Bao Xuan Ng , Silvana Xinyi Choo , Shamala Thilarajah This is my paper · ORCID
classification cs.HCcs.AIcs.LG
keywords explanationsexample-basedinteractiveonboardinghealthprofessionalscollaborativedecision
verification ladder T0 review T1 audit T2 compute T3 formal
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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 and reliance on AI. Our interactive example-based explanations during onboarding assisted health professionals in having a better reliance on AI and making a higher ratio of making `right' decisions and a lower ratio of `wrong' decisions than providing only feature-based explanations during the decision-support phase. Our study discusses new challenges of assisting user's onboarding with AI for human-AI collaborative decision-making.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Preview

    cs.HC 2026-04 conditional novelty 6.0 of 10

    Rule-guided mismatch cues raise Human+AI rehab-assessment accuracy by 14% and cut harmful reliance; prospective embedding previews raise local model-edit gains from 11.5% to 36%, with global transfer often regressing.

  2. Towards Uncertainty Aware Task Delegation and Human-AI Collaborative Decision-Making

    cs.HC 2025-05 conditional novelty 5.0 of 10

    Distance-based uncertainty visualizations with interactive examples improved correct decisions by 8.20 percentage points over numeric confidence scores in a 27-participant stroke rehabilitation study.

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