As machine learning black boxes are increasingly being deployed in domains such as
healthcare and criminal justice, there is growing emphasis on building tools and techniques for …

As practitioners increasingly deploy machine learning models in critical domains such as
health care, finance, and policy, it becomes vital to ensure that domain experts function …

The increasing adoption of machine learning tools has led to calls for accountability via model
interpretability. But what does it mean for a machine learning model to be interpretable by …

Practitioners increasingly use machine learning (ML) models, yet models have become
more complex and harder to understand. To understand complex models, researchers have …

Counterfactual explanations are emerging as an attractive option for providing recourse to
individuals adversely impacted by algorithmic decisions. As they are deployed in critical …

As black box explanations are increasingly being employed to establish model credibility in
high stakes settings, it is important to ensure that these explanations are accurate and …

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing
complex tasks. Moreover, recent research has shown that incorporating human-annotated …

Motivated by concerns surrounding the fairness effects of sharing and transferring fair machine
learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The first is …

Language modeling is a keystone task in natural language processing. When training a
language model on sensitive information, differential privacy (DP) allows us to quantify the …

Autonomous experimentation systems use algorithms and data from prior experiments to
select and perform new experiments in order to meet a specified objective. In most …