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

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

Predictive multiplicity occurs when classification models with statistically indistinguishable
performances assign conflicting predictions to individual samples. When used for decision …

Abstract Machine learning tasks may admit multiple competing models that achieve similar
performance yet produce conflicting outputs for individual samples---a phenomenon known …

Statistical measures for group fairness in machine learning reflect the gap in performance of
algorithms across different groups. These measures, however, exhibit a high variance …

Information about us, our actions, and our preferences is created at scale through surveys or
scientific studies or as a result of our interaction with digital devices such as smartphones …

Prediction models have been widely adopted as the basis for decision-making in domains
as diverse as employment, education, lending, and health. Yet, few real world problems …

Abstract Machine learning models are often used to inform real world risk assessment tasks:
predicting consumer default risk, predicting whether a person suffers from a serious illness …

Variance in predictions across different trained models is a significant, under-explored
source of error in fair binary classification. In practice, the variance on some data examples …

The design decisions of developers and researchers in creating algorithmic tools—like
constructing variables, performing feature selection, and binning model outputs—are …