Recent studies have indicated that foundation models, such as BERT and GPT, excel at
adapting to various downstream tasks. This adaptability has made them a dominant force in …

Artificial intelligence (AI) systems are trained to solve complex problems and learn to
perform specific tasks by using large volumes of data, such as prediction, classification …

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 …

Existing model evaluation tools mainly focus on evaluating classification models, leaving a
gap in evaluating more complex models, such as object detection. In this paper, we develop …

Large Language Models (LLMs) like GPT-4 which support multimodal input (ie, prompts
containing images in addition to text) have immense potential to advance visualization …

Machine learning (ML) models can fail in unexpected ways in the real world, but not all
model failures are equal. With finite time and resources, ML practitioners are forced to …

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

Data-centric artificial intelligence (AI)(DCAI) has the potential to bring significant benefits to
society; however, it also poses significant challenges and potential risks. It is crucial to …

Despite large progress in Explainable and Safe AI, practitioners suffer from a lack of
regulation and standards for AI safety. In this work we merge recent regulation efforts by the …

Credit scoring is a prominent problem in machine learning (ML). ML classifiers used to
assess creditworthiness need to be evaluated using appropriate metrics. Common metrics …