Abstract Machine learning algorithms are expected to improve referral decisions. In this
article I discuss the legitimacy of deferring referral decisions in primary care to …

The now well-known impossibility results of algorithmic fairness demonstrate that an error-
prone predictive model cannot simultaneously satisfy two plausible conditions for group …

What does it mean for algorithmic classifications to be fair to different socially salient
groups? According to classification parity criteria, what is required is equality across groups …

Group fairness metrics are an established way of assessing the fairness of prediction-based
decision-making systems. However, these metrics are still insufficiently linked to …

This article presents a fairness principle for evaluating decision-making based on
predictions: a decision rule is unfair when the individuals directly impacted by the decisions …

This paper examines racial discrimination and algorithmic bias in predictive policing
algorithms (PPAs), an emerging technology designed to predict threats and suggest …

In prediction-based decision-making systems, different perspectives can be at odds: The
short-term business goals of the decision makers are often in conflict with the decision …

It is expected that machine learning algorithms will enable better diagnosis, prognosis, and
treatment in psychiatry. A central argument for deploying algorithmic methods in clinical …

Recently, there has been much discussion of 'fair machine learning': fairness in data‐driven
decision‐making systems (which are often, though not always, made with assistance from …

Brian Hedden, in a recent article in Philosophy and Public Affairs [Hedden.“On Statistical
Criteria of Algorithmic Fairness.” Philosophy and Public Affairs 49 (2): 209–231. https://doi …