相关论文: Using Processing Fluency as a Metric of Trust in S…

In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of…

Micro-task crowdsourcing has become a successful mean to obtain high-quality data from a large crowd of diverse people. In this context, trust between all the involved actors (i.e. requesters, workers, and platform owners) is a critical…

Business Process Visualisations (BPVs) have become indispensable tools for organisations seeking to enhance their operational efficiency, decision-making capabilities, and overall performance. The burgeoning interest in process modeling and…

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…

This paper presents an argument for why we are not measuring trust sufficiently in explainability, interpretability, and transparency research. Most studies ask participants to complete a trust scale to rate their trust of a model that has…

Diffusion models have demonstrated remarkable performance in generation tasks. Nevertheless, explaining the diffusion process remains challenging due to it being a sequence of denoising noisy images that are difficult for experts to…

Unsupervised learning from continuous visual streams is a challenging problem that cannot be naturally and efficiently managed in the classic batch-mode setting of computation. The information stream must be carefully processed accordingly…

Empirical studies form an integral part of visualization research. Not only can they facilitate the evaluation of various designs, techniques, systems, and practices in visualization, but they can also enable the discovery of the…

Trustworthiness and trust are basic factors in common societies that allow us to interact and enjoy being in crowds without fear. As robotic devices start percolating into our daily lives they must behave as fully trustworthy objects, such…

People's trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling. However, to what extent and why domain expertise impacts…

We build on theoretical foundations of tool-mediated learning, tool design, and human computer interaction to develop a framework for implicit scaffolding in learning environments. Implicit scaffolding employs affordances, constraints,…

Advances in machine learning have enabled the creation of realistic synthetic videos known as deepfakes. As deepfakes proliferate, concerns about rapid spread of disinformation and manipulation of public perception are mounting. Despite the…

Understanding correlation judgement is important to designing effective visualizations of bivariate data. Prior work on correlation perception has not considered how factors including prior beliefs and uncertainty representation impact such…

While machine learning approaches to visual emotion recognition offer great promise, current methods consider training and testing models on small scale datasets covering limited visual emotion concepts. Our analysis identifies an important…

Trust and perceived safety play a crucial role in the public acceptance of automated vehicles. To understand perceived risk, an experiment was conducted using a driving simulator under two automated driving styles and optionally introducing…

Dirty data and deceptive design practices can undermine, invert, or invalidate the purported messages of charts and graphs. These failures can arise silently: a conclusion derived from a particular visualization may look plausible unless…

Explanations are central to improving transparency, trust, and user satisfaction in recommender systems (RS), yet it remains unclear how different explanation formats (visual vs. textual) are suited to users with different personal…

Research on cognitive biases and heuristics has become increasingly popular in the visualization literature in recent years. Researchers have studied the effects of biases on visualization interpretation and subsequent decision-making.…

We investigate the perceived visual complexity (VC) in data visualizations using objective image-based metrics. We collected VC scores through a large-scale crowdsourcing experiment involving 349 participants and 1,800 visualization images.…

This paper proposes a novel framework to evaluate fluid simulation methods based on crowd-sourced user studies in order to robustly gather large numbers of opinions. The key idea for a robust and reliable evaluation is to use a reference…