Researchers have developed a novel video-based scaffolding protocol to elicit explainable AI (XAI) requirements from stroke survivors, addressing the challenges posed by acquired communication disorders. This protocol utilizes four scaffolding approaches—analogical bridging, projective personas, binary forcing, and extended response time—to help patients and caregivers articulate their needs for algorithmic transparency. The study identified heterogeneous XAI requirements and also revealed three systematic facilitation biases that practitioners should be aware of, contributing a reusable methodology for designing trustworthy human-machine systems in rehabilitation. AI
IMPACT This research offers a new methodology for designing more trustworthy AI systems in healthcare, specifically for stroke rehabilitation.
RANK_REASON The cluster contains an academic paper detailing a new methodology for eliciting XAI requirements. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Caregivers and Veterans Omnibus Health Services Act of 2010
- human–computer interaction
- SpaceXAI
- Stroke Survivors and Caregivers Using an Online Mindfulness-based Intervention Together
- Yogesh Kumar Meena
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