Explainable Artificial Intelligence (XAI) processes typically combine various explanation and verification strategies to support the analysis in different domains. Due to the increasing number of techniques and the variety of XAI methods deployed, …
Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is. Recent research has revealed some heuristics and biases of these models. However, to date, there …
With the recent release of AI interaction guidelines from Apple, Google, and Microsoft, there is clearly interest in understanding the best practices in human-AI interaction. However, industry standards are not determined by a single company, but …
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual …
In this paper, we bridge the gap between visualization and musicology by focusing on rhythm analysis tasks, which are tedious due to the complex visual encoding of the well-established Common Music Notation (CMN). Instead of replacing the CMN, we …
In supervised learning, to ensure the model's validity, it is essential to identify dataset shifts, i.e., when the data distribution changes from the one the model encountered at the time of training. To detect such changes, a comparative analysis of …
We present a mixed-initiative approach to interactive annotation of argumentation, a typically time-consuming manual task. Our system facilitates the process by suggesting which fragments of text to annotate next. Suggestions are sourced from …
Guidance processes in visual analytics applications often lack adaptivity. In this position paper, we contribute the concept of co-adaptive guidance, building on the principles of initiation and adaptation. We argue that both the user and the system …
The development of linguistic corpora is fraught with various problems of annotation and representation. These constitute a very real challenge for the development and use of annotated corpora, but as yet not much literature exists on how to address …
Multi-target classification of multivariate time series data poses a challenge in many real-world applications (e.g., predictive maintenance). Machine learning methods, such as random forests and neural networks, support training these classifiers. …