Related papers: Coping with Uncertainty in UX Design Practice: Pra…

This paper addresses the challenge of jointly modeling user intent diversity and behavioral uncertainty in recommender systems. A unified representation learning framework is proposed. The framework builds a multi-intent representation…

Model uncertainty is a crucial issue in statistics, econometrics and machine learning, yet its definition remains ambiguous and is subject to various interpretations in the literature. So far, there has not been a universally accepted…

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in…

Recommender systems are playing an increasingly important role in alleviating information overload and supporting users' various needs, e.g., consumption, socialization, and entertainment. However, limited research focuses on how values…

Data-driven predictions are often perceived as inaccurate in hindsight due to behavioral responses. In this study, we explore the role of interface design choices in shaping individuals' decision-making processes in response to predictions…

In video recommendation, a critical component that determines the system's recommendation accuracy is the watch-time prediction module, since how long a user watches a video directly reflects personalized preferences. One of the key…

In recent years, discussions on integrating Artificial Intelligence (AI) into UX design have intensified. However, the practical application of AI tools in design is limited by their operation within overly simplified scenarios, inherent…

Decision-makers abhor uncertainty, and it is certainly true that the less there is of it the better. However, recognizing that uncertainty is part of the equation, particularly for deciding on environmental policy, is a prerequisite for…

Although empirical research often underpins practical visualization guidelines, it remains unclear how well these research-driven insights are reflected in the guidelines practitioners actually use. In this paper, we investigate the…

Quantifying the uncertainty of supervised learning models plays an important role in making more reliable predictions. Epistemic uncertainty, which usually is due to insufficient knowledge about the model, can be reduced by collecting more…

Artificial intelligence (AI) has revolutionized decision-making processes and systems throughout society and, in particular, has emerged as a significant technology in high-impact scenarios of national interest. Yet, despite AI's impressive…

Programs for complicated engineering and scientific tasks always have to deal with a problem of showing numerous graphical results. The limits of the screen space and often opposite requirements from different users are the cause of the…

The well-established deceptive design literature has focused on conventional user interfaces. With the rise of extended reality (XR), understanding deceptive design's unique manifestations in this immersive domain is crucial. However,…

How do we know how much we know? Quantifying uncertainty associated with our modelling work is the only way we can answer how much we know about any phenomenon. With quantitative science now highly influential in the public sphere and the…

This paper proposes discomfort as a new material for HCI researchers and designers to consider in any application that helps a person develop a new skill, practice or state. Discomfort is a fundamental precursor of adaptation and adaptation…

It is common to encounter the situation with uncertainty for decision makers (DMs) in dealing with a complex decision making problem. The existing evidence shows that people usually fear the extreme uncertainty named as the unknown. This…

Clinician-facing predictive models are increasingly present in the healthcare setting. Regardless of their success with respect to performance metrics, all models have uncertainty. We investigate how to visually communicate uncertainty in…

There is a rapidly growing literature on dark patterns, user interface designs -- typically related to shopping or privacy -- that researchers deem problematic. Recent work has been predominantly descriptive, documenting and categorizing…

Modelling uncertainty in Machine Learning models is essential for achieving safe and reliable predictions. Most research on uncertainty focuses on output uncertainty (predictions), but minimal attention is paid to uncertainty at inputs. We…

Better representation of the uncertainty in a data visualisation is a focus of recent research activity. A problem with the current literature is that there is a lack of clarity about the definition of uncertainty and what it means to…