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xai-primer.com — A Visual Ideation Space of Interactive Explainers

A summary of the main functions of the XAI Primer. The design space is structured into three layers, namely: the cluster layer (a), the item layer (b) and the network layer (c) that can be explored in an open-ended and serendipitous way. Guided tours …

Curating Publications as Artefacts—Exploring Machine Learning Research in an Interactive Virtual Museum

The need for innovating scientific publications is felt across various research fields. In the last thirty years, the publishing process is accelerating, and new research is coming out every day. In Machine Learning, some attempts have been made to …

Speculative Execution of Similarity Queries: Real-Time Parameter Optimization through Visual Exploration

The parameters of complex analytical models often have an unpredictable influence on the models’ results, rendering parameter tuning a non-intuitive task. By concurrently visualizing both the model and its results, visual analytics tackles this …

The Explainable Artificial Intelligence Primer. A Digital Ideation Space for Explainable Artificial Intelligence Strategies

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, …

XplaiNLI: Explainable Natural Language Inference through Visual Analytics

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 …

A Comparative Analysis of Industry Human-AI Interaction Guidelines

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 …

An empirical study of explainable AI techniques on deep learning models for time series tasks

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 …

Augmenting Sheet Music with Rhythmic Fingerprints

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 …

DataShiftExplorer: Visualizing and Comparing Change in Multidimensional Data for Supervised Learning

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 …

Guided Linguistic Annotation of Argumentation through Visual Analytics

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 …