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Interactive Example-based Explanations to Improve Health Professionals' Onboarding with AI for Human-AI Collaborative Decision Making
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.
Exploring a Multimodal Fusion-based Deep Learning Network for Detecting Facial Palsy
Our experimental results show that among various data modalities (i. e. unstructured data - RGB images and images of facial line segments and structured data - coordinates of facial landmarks and features of facial expressions), the feed-forward neural network using features of facial expression achieved the highest precision of 76. 22 while the ResNet-based model using images of facial line segments achieved the highest recall of 83. 47.
Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making
With advanced AI/ML, there has been growing research on explainable AI (XAI) and studies on how humans interact with AI and XAI for effective human-AI collaborative decision-making.
Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making
Our results showed that the AI model with both salient features and counterfactual explanations assisted therapists and laypersons to improve their performance and agreement level on the task when `right' AI outputs are presented.
Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises
In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation.
Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises
Explainable artificial intelligence (AI) techniques are increasingly being explored to provide insights into why AI and machine learning (ML) models provide a certain outcome in various applications.
Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders
In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them in imagining new uses of data and technologies in the child welfare system.
Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction.
Designing Personalized Interaction of a Socially Assistive Robot for Stroke Rehabilitation Therapy
The research of a socially assistive robot has a potential to augment and assist physical therapy sessions for patients with neurological and musculoskeletal problems (e. g. stroke).
Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment
Rehabilitation assessment is critical to determine an adequate intervention for a patient.
An Approach for Self-Training Audio Event Detectors Using Web Data
The audio event detectors are trained on the labeled audio and ran on the unlabeled audio downloaded from YouTube.
