Whether AI explanations can help users achieve specific tasks efficiently (ie, usable
explanations) is significantly influenced by their visual presentation. While many techniques …

Examining the coexisting policies for mandatory and voluntary disclosure of environmental
information, this paper focuses on the unique background for such disclosure in the context …

With the rise of AI, algorithms have become better at learning underlying patterns from the
training data including ingrained social biases based on gender, race, etc. Deployment of …

The past decade has witnessed a plethora of works that leverage the power of visualization
(VIS) to interpret machine learning (ML) models. The corresponding research topic, VIS4ML …

Bias in machine learning has rightly received significant attention over the past decade.
However, most fair machine learning (fair-ML) works to address bias in decision-making …

The exponentially increasing advances in robotics and machine learning are facilitating the
transition of robots from being confined to controlled industrial spaces to performing novel …

The increasing reliance on Large Language Models (LLMs) for health information seeking
can pose severe risks due to the potential for misinformation and the complexity of these …

Graph mining is an essential component of recommender systems and search engines.
Outputs of graph mining models typically provide a ranked list sorted by each item's …

Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias.
As a consequence, disparate stakeholders need to interact with and make informed …

Latent vectors extracted by machine learning (ML) are widely used in data exploration (eg, t-
SNE) but suffer from a lack of interpretability. While previous studies employed disentangled …