Josua Krause

Bloomberg · AI

Recently, there is growing consensus of the critical need to have better techniques to explain machine learning models. However, many of the popular techniques are instance-level explanations, which explain the model from the point of view of a single data point. While local explanations may be misleading, they are also not human-scale, as it is im...

As machine learning models increase in complexity, the human ability to understand and interpret decisions made by those models has not been able to keep up. The usefulness of white-box analysis techniques, exposing the internal state of models, is limited to relatively simple models and has trouble with complex models, such as deep neural networks...

To realize the full potential of machine learning in diverse real-world domains, it is necessary for model predictions to be readily interpretable and actionable for the human in the loop. Analysts, who are the users but not the developers of machine learning models, often do not trust a model because of the lack of transparency in associating pred...

Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverag...

Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary classifier. The approach leverag...

The recent successes of machine learning (ML) have exposed the need for making models more interpretable and accessible to different stakeholders in the data science ecosystem, like domain experts, data analysts, and even the general public. The assumption here is that higher interpretability will lead to more confident human decision-making based...

Dealing with the curse of dimensionality is a key challenge in high-dimensional data visualization. We present SeekAView to address three main gaps in the existing research literature. First, automated methods like dimensionality reduction or clustering suffer from a lack of transparency in letting analysts interact with their outputs in real-time...

It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing predictive quality. We identify...

Understanding predictive models, in terms of interpreting and identifying actionable insights, is a challenging task. Often the importance of a feature in a model is only a rough estimate condensed into one number. However, our research goes beyond these naïve estimates through the design and implementation of an interactive visual analytics system...

We present a study aimed at understanding how human observers judge scatter plot similarity when presented with a large set of iconic scatter plot representations. The work we present involves 18 participants with a scientific background in a similarity perception study. The study asks participants to group a carefully selected set of plots accordi...

Many researchers across diverse disciplines aim to analyze the behavior of cohorts whose behaviors are recorded in large event databases. However, extracting cohorts from databases is a difficult yet important step, often overlooked in many analytical solutions. This is especially true when researchers wish to restrict their cohorts to exhibit a pa...