Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

Machine learning (ML) interpretability techniques can reveal undesirable patterns in data
that models exploit to make predictions-potentially causing harms once deployed. However …

Missing values are a fundamental problem in data science. Many datasets have missing
values that must be properly handled because the way missing values are treated can have …

How can we better organize code in computational notebooks? Notebooks have become a
popular tool among data scientists, as they seamlessly weave text and code together …

As machine learning (ML) pipelines affect an increasing array of stakeholders, there is a
growing need for documenting how input from stakeholders is recorded and incorporated …

Given thousands of equally accurate machine learning (ML) models, how can users choose
among them? A recent ML technique enables domain experts and data scientists to …

In real applications, interaction between machine learning models and domain experts is
critical; however, the classical machine learning paradigm that usually produces only a …

Anomaly detection (AD) plays an important role in numerous applications. In this paper, we
focus on two understudied aspects of AD that are critical for integration into real-world …

People's decision-making abilities often fail to improve or may even erode when they rely on
AI for decision-support, even when the AI provides informative explanations. We argue this …

How can we develop visual analytics (VA) tools that can be easily adopted? Visualization
researchers have developed a large number of web-based VA tools to help data scientists in …