Physiological Indicators for User Trust in Machine Learning with Influence Enhanced Fact-Checking

© IFIP International Federation for Information Processing 2019. Trustworthy Machine Learning (ML) is one of significant challenges of “black-box” ML for its wide impact on practical applications. This paper investigates the effects of presentation of influence of training data points on machine learning predictions to boost user trust. A framework of fact-checking for boosting user trust is proposed in a predictive decision making scenario to allow users to interactively check the training data points with different influences on the prediction by using parallel coordinates based visualization. This work also investigates the feasibility of physiological signals such as Galvanic Skin Response (GSR) and Blood Volume Pulse (BVP) as indicators for user trust in predictive decision making. A user study found that the presentation of influences of training data points significantly increases the user trust in predictions, but only for training data points with higher influence values under the high model performance condition, where users can justify their actions with more similar facts to the testing data point. The physiological signal analysis showed that GSR and BVP features correlate to user trust under different influence and model performance conditions. These findings suggest that physiological indicators can be integrated into the user interface of AI applications to automatically communicate user trust variations in predictive decision making

User Interface Challenges of Banking ATM Systems in Nigeria

The use of banking automated teller machine (ATM) technological innovations have significant importance and benefits in Nigeria, but numerous investigations have shown that illiterate and semiliterate Nigerians do not perceive them as useful or easy-to-use. Developing easy-to-use banking ATM system interfaces is essential to accommodate over 40% illiterate and semiliterate Nigerians, who are potential users of banking ATM systems. The purpose of this study was to identify strategies software developers of banking ATM systems in Nigeria use to create easy-to-use banking ATM system interfaces for a variety of people with varying abilities and literacy levels. The technology acceptance model was adopted as the conceptual framework. The study\u27s population consisted of qualified and experienced developers of banking ATM system interfaces chosen from 1 organization in Enugu, Nigeria. The data collection process included semistructured, in-depth face-to-face interviews with 9 banking ATM system interface developers and the analysis of 11 documents: 5 from participant case organizations and 6 from nonparticipant case organizations. Member checking was used to increase the validity of the findings from the participants. Through methodological triangulation, 4 major themes emerged from the study: importance of user-centered design strategies, importance of user feedback as essential interface design, value of pictorial images and voice prompts, and importance of well-defined interface development process. The findings in this study may be beneficial for the future development of strategies to create easy-to-use ATM system interfaces for a variety of people with varying abilities and literacy levels and for other information technology systems that are user interface technology dependent

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks