Data storytelling is powerful for communicating data insights, but it requires diverse skills
and considerable effort from human creators. Recent research has widely explored the …

In explainable machine learning, local post-hoc explanation algorithms and inherently
interpretable models are often seen as competing approaches. This work offers a partial …

Machine learning (ML) recourse techniques are increasingly used in high-stakes domains,
providing end users with actions to alter ML predictions, but they assume ML developers …

Computational notebooks, such as Jupyter Notebook, have become data scientists' de facto
programming environments. Many visualization researchers and practitioners have …

Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and
building AI-powered applications easier than ever before. However, identifying potential …

Explanations in interactive machine-learning systems facilitate debugging and improving
prediction models. However, the effectiveness of various global model-centric and data …

Electric load forecasting is an indispensable component of electric power system planning
and management. Inaccurate load forecasting may lead to the threat of outages or a waste …

Abstract Generalized Additive Models (GAMs) have recently experienced a resurgence in
popularity due to their interpretability, which arises from expressing the target value as a …

In the realm of interactive machine-learning systems, the provision of explanations serves as
a vital aid in the processes of debugging and enhancing prediction models. However, the …

Data entry constitutes a fundamental component of the machine learning pipeline, yet it
frequently results in the introduction of labelling errors. When a model has been trained on a …