Eliciting explainable AI (XAI) requirements from stroke survivors presents a methodological challenge with direct implications for the design of trustworthy brain-computer interfaces for rehabilitation. How can patients and caregivers articulate preferences about algorithmic transparency when they lack conceptual frameworks for explainability, and when standard elicitation approaches are structurally inadequate for users with acquired communication disorders?
arXiv:2606. 14306v1 Announce Type: cross Abstract: Current Generative AI (GenAI) interfaces remain largely constrained to chatbox interaction, which can impose high cognitive demands on users and create substantial barriers for people with intellectual disabilities (ID), including prompt formulation difficulties, response overload, and limited mechanisms to assess information reliability.
By Virginia Francisco, Daniel Guasch, Raquel Herv\'as
arXiv:2606. 24960v1 Announce Type: new Abstract: Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed.
By Tamim Ahmed, Thanassis Rikakis
arXiv:2608.24555v1 Announce Type: cross
Abstract: Prehospital stroke assessment aims to accurately identify stroke symptoms and make rapid decisions through standardized procedures within an extremel...
By Wentao Yang, Zhenye Xu, Ruoyi Li, Musen Zhang, Yao Guo
The paper argues that deploying generative AI agents requires more than isolated task success; they must remain useful across repeated interactions, changing conditions, and dependencies on people within shared workflows. The authors introduce two complementary evaluation aspects—operational resilience and considerate participation—to assess how agents recover from blocked work, communicate limits, and adapt to affected people and role boundaries. Using 120 simulated healthcare trajectories across two AI models and twelve stakeholder-derived tasks under varying challenge levels, the study finds that agents shift toward greater human dependence and increased workload as challenge accumulates, while also broadening from task-focused adaptation to task reframing and wider coordination.
By Yuanchen Bai, Zijian Ding, Angelique Taylor
arXiv:2606. 01171v1 Announce Type: cross Abstract: Artificial intelligence (AI) can reproduce and amplify the structural inequities faced by minoritized communities.
By Tijs Portegies, Laureanne Willems, Maaike Harbers, Giovanni Sileno, Roland van Dierendonck, Mayesha Tasnim, Lotte Willemsen, Sennay Ghebreab