Prediction algorithms are regularly used to support and automate high-stakes policy
decisions about the allocation of scarce public resources. However, data-driven decision …

The use of algorithms to support prediction-based decision-making is becoming
commonplace in a range of domains including health, criminal justice, education, social …

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 …

In the field of algorithmic fairness, many fairness criteria have been proposed. Oftentimes,
their proposal is only accompanied by a loose link to ideas from moral philosophy--which …

In this paper, we provide a moral analysis of two criteria of statistical fairness debated in the
machine learning literature: 1) calibration between groups and 2) equality of false positive …

I consider statistical criteria of algorithmic fairness from the perspective of the ideals of
fairness to which these criteria are committed. I distinguish and describe three theoretical …

Ensuring fairness of prediction-based decision making is based on statistical group fairness
criteria. Which one of these criteria is the morally most appropriate one depends on the …

Abstract National Statistical Organizations (NSOs) increasingly draw on Machine Learning
(ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML …

As algorithms are increasingly enlisted to make critical determinations about human actors,
the more frequently we see these algorithms appear in sensational headlines crying foul on …

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