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Author: 9108

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User Reliance on AI Support for Collaborative Partner Selection

Whether choosing teammates for a project or partners for everyday life tasks, people constantly decide with whom to work. However, in these decisions, they often overemphasize characteristics that are not directly relevant to task performance. For example, prioritizing a partner’s trustworthiness for a task where comp…

Tiffany Matej Hrkalovic et al.Jheronimus Academy of Data Science

Benefits of Machine Learning Explanations: Improved Learning in an AI-assisted Sequence Prediction Task

Research in Explainable AI (XAI) has shown that explanations can improve users' understanding of AI models, improve user performance and potentially reduce overreliance on AI predictions. However, this is mostly evaluated by static rather than dynamic measures, and the role of XAI on learning over trials is rarely stu…

Yu Liang et al.Technical University Eindhoven

Good Performance Isn't Enough to Trust AI: Lessons from Logistics Experts on their Long-Term Collaboration with an AI Planning System

While research on trust in human-AI interactions is gaining recognition, much work is conducted in lab settings that, therefore, lack ecological validity and often omit the trust development perspective. We investigated a real-world case in which logistics experts had worked with an AI system for several years (in som…

Patricia K. Kahr et al.Technical University Eindhoven

The Trust Recovery Journey. The Effect of the Timing of Errors on the Willingness to Follow AI Advice.

Complementing human decision-making with AI advice offers substantial advantages. However, humans do not always trust AI advice appropriately and are overly sensitive to incidental AI errors, even in cases with overall good performance. Today's research still needs to uncover the underlying aspects of trust decline an…

Patricia K. Kahr et al.Technical University Eindhoven

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Leveraging ChatGPT for Automated Human-centered Explanations in Recommender Systems

The adoption of recommender systems (RSs) in various domains has become increasingly popular, but concerns have been raised about their lack of transparency and interpretability. While significant advancements have been made in creating explainable RSs, there is still a shortage of automated approaches that can delive…

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Ítallo Silva et al.Federal University of Campina Grande

Benefits of Human-AI Interaction for Expert Users Interacting with Prediction Models: a Study on Marathon Running

Users with large domain knowledge can be reluctant to use prediction models. This also applies to the sports domain, where running coaches rarely rely on marathon prediction tools for race-plan advice for their runners' next marathon. This paper studies the effect of adding interactivity to such prediction models, to…

Heleen Muijlwijk et al.Technical University Eindhoven

Improving understandability of feature contributions in model-agnostic explainable AI tools

Model-agnostic explainable AI tools explain their predictions by means of ’local’ feature contributions. We empirically investigate two potential improvements over current approaches. The first one is to always present feature contributions in terms of the contribution to the outcome that is perceived as positive by t…

Sophia Hadash et al.Jheronimus Academy of Data Science

Interactive music genre exploration with visualization and mood control

Recommender systems can be used to help users discover novel items and explore new tastes, for example in music genre exploration. However, little work has studied how to improve users' understandability and acceptance of novel items. In this paper, we investigate how visualization as well as mood control affects the…

Yu Liang et al.Technical University Eindhoven

Overlooking context: How do Defaults and Framing Reduce Deliberation in Smart Home Privacy Decision-Making?

Research has demonstrated that users' heuristic decision-making processes cause external factors like defaults and framing to influence the outcome of privacy decisions. Proponents of ``privacy nudging'' have proposed leveraging these effects to guide users' decisions. Our research shows that defaults and framing not…

Paritosh Bahirat et al.Clemson University

Beyond Behavior: The Coach's Perspective on Technology in Health Coaching

Rapid innovations in electronic healthcare and behavior tracking systems are challenging health coaches (dietitians, personal trainers, etc.) to rethink their traditional roles and healthcare practices. At the same time, many current e-coaching systems have been developed without explicitly incorporating the healthcar…

Heleen Rutjes et al.Technical University Eindhoven

Rasch-based Tailored Goals for Nutrition Assistance Systems

Choosing adequate goals plays is central to the success of a task. With this study, we investigate tailoring the goals of a nutrition assistance system to the user's abilities according to a Rasch scale. To that end, we evaluated two versions of a mobile system that offers dietary tracking, visual feedback, and person…

Hanna Schaefer et al.

Department of Informatics

Paper Title	Authors	Research Topics	Paper Database	Year
User Reliance on AI Support for Collaborative Partner Selection Whether choosing teammates for a project or partners for everyday life tasks, people constantly decide with whom to work. However, in these decisions, they often overemphasize characteristics that are not directly relevant to task performance. For example, prioritizing a partner’s trustworthiness for a task where comp…	TH Tiffany Matej Hrkalovic et al.Jheronimus Academy of Data Science
Benefits of Machine Learning Explanations: Improved Learning in an AI-assisted Sequence Prediction Task Research in Explainable AI (XAI) has shown that explanations can improve users' understanding of AI models, improve user performance and potentially reduce overreliance on AI predictions. However, this is mostly evaluated by static rather than dynamic measures, and the role of XAI on learning over trials is rarely stu…	YL Yu Liang et al.Technical University Eindhoven
Good Performance Isn't Enough to Trust AI: Lessons from Logistics Experts on their Long-Term Collaboration with an AI Planning System While research on trust in human-AI interactions is gaining recognition, much work is conducted in lab settings that, therefore, lack ecological validity and often omit the trust development perspective. We investigated a real-world case in which logistics experts had worked with an AI system for several years (in som…	PK Patricia K. Kahr et al.Technical University Eindhoven
The Trust Recovery Journey. The Effect of the Timing of Errors on the Willingness to Follow AI Advice. Complementing human decision-making with AI advice offers substantial advantages. However, humans do not always trust AI advice appropriately and are overly sensitive to incidental AI errors, even in cases with overall good performance. Today's research still needs to uncover the underlying aspects of trust decline an…	PK Patricia K. Kahr et al.Technical University Eindhoven
AdRecommended Learn AI Coding at CodeNow Structured lessons, hands-on projects, and continuous updates for people bringing AI into real development work. Explore Now
Leveraging ChatGPT for Automated Human-centered Explanations in Recommender Systems The adoption of recommender systems (RSs) in various domains has become increasingly popular, but concerns have been raised about their lack of transparency and interpretability. While significant advancements have been made in creating explainable RSs, there is still a shortage of automated approaches that can delive…	ÍS Ítallo Silva et al.Federal University of Campina Grande
Benefits of Human-AI Interaction for Expert Users Interacting with Prediction Models: a Study on Marathon Running Users with large domain knowledge can be reluctant to use prediction models. This also applies to the sports domain, where running coaches rarely rely on marathon prediction tools for race-plan advice for their runners' next marathon. This paper studies the effect of adding interactivity to such prediction models, to…	HM Heleen Muijlwijk et al.Technical University Eindhoven
Improving understandability of feature contributions in model-agnostic explainable AI tools Model-agnostic explainable AI tools explain their predictions by means of ’local’ feature contributions. We empirically investigate two potential improvements over current approaches. The first one is to always present feature contributions in terms of the contribution to the outcome that is perceived as positive by t…	SH Sophia Hadash et al.Jheronimus Academy of Data Science
Interactive music genre exploration with visualization and mood control Recommender systems can be used to help users discover novel items and explore new tastes, for example in music genre exploration. However, little work has studied how to improve users' understandability and acceptance of novel items. In this paper, we investigate how visualization as well as mood control affects the…	YL Yu Liang et al.Technical University Eindhoven
Overlooking context: How do Defaults and Framing Reduce Deliberation in Smart Home Privacy Decision-Making? Research has demonstrated that users' heuristic decision-making processes cause external factors like defaults and framing to influence the outcome of privacy decisions. Proponents of ``privacy nudging'' have proposed leveraging these effects to guide users' decisions. Our research shows that defaults and framing not…	PB Paritosh Bahirat et al.Clemson University
Beyond Behavior: The Coach's Perspective on Technology in Health Coaching Rapid innovations in electronic healthcare and behavior tracking systems are challenging health coaches (dietitians, personal trainers, etc.) to rethink their traditional roles and healthcare practices. At the same time, many current e-coaching systems have been developed without explicitly incorporating the healthcar…	HR Heleen Rutjes et al.Technical University Eindhoven
Rasch-based Tailored Goals for Nutrition Assistance Systems Choosing adequate goals plays is central to the success of a task. With this study, we investigate tailoring the goals of a nutrition assistance system to the user's abilities according to a Rasch scale. To that end, we evaluated two versions of a mobile system that offers dietary tracking, visual feedback, and person…	HS Hanna Schaefer et al.Department of Informatics