As individuals and communities interact in and with an environment that is increasingly
virtual, they are often vulnerable to the commodification of their digital footprint. Concepts …

Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …

In 1996, Accountability in a Computerized Society [95] issued a clarion call concerning the
erosion of accountability in society due to the ubiquitous delegation of consequential …

Across machine learning (ML) sub-disciplines, researchers make explicit mathematical
assumptions in order to facilitate proof-writing. We note that, specifically in the area of …

Recent work on algorithmic fairness has largely focused on the fairness of discrete
decisions, or classifications. While such decisions are often based on risk score models, the …

Introduction Developments in Artificial Intelligence (AI) are adopted widely in healthcare.
However, the introduction and use of AI may come with biases and disparities, resulting in …

Modern Healthcare cyberphysical systems have begun to rely more and more on distributed
AI leveraging the power of Federated Learning (FL). Its ability to train Machine Learning …

Legal literature on machine learning (ML) tends to focus on harms, and thus tends to reason
about individual model outcomes and summary error rates. This focus has masked important …

Trade-offs between accuracy and efficiency pervade law, public health, and other non-
computing domains, which have developed policies to guide how to balance the two in …

We study the problem of post-processing a supervised machine-learned regressor to
maximize fair binary classification at all decision thresholds. By decreasing the statistical …