As inductive decision-making procedures, the inferences made by machine learning
programs are subject to underdetermination by evidence and bear inductive risk. One …

Recent scholarship in philosophy of science and technology has shown that scientific and
technological decision making are laden with values, including values of a social, political …

As risk assessments become increasingly recommended and adopted as a tool for criminal
justice reform, the technical community and advocates alike must ask the right questions …

As machine learning informs increasingly consequential decisions, different metrics have
been proposed for measuring algorithmic bias or unfairness. Two popular “fairness …

Over the last few years, legal scholars, policy-makers, activists and others have generated a
vast and rapidly expanding literature concerning the ethical ramifications of using artificial …

The widespread deployment of machine learning models in high-stakes decision making
scenarios requires a code of ethics for machine learning practitioners. We identify four of the …

Risk assessments have proliferated in the United States criminal justice system. The theory
of change motivating their adoption involves two key assumptions: first, that risk …

We argue that the growing prevalence of statistical machine learning in everyday decision
making–from creditworthiness to police force allocation–effectively replaces many of our …

Law-enforcement agencies are increasingly able to leverage crime statistics to make risk
predictions for particular individuals, employing a form of inference that some condemn as …

Recent work on fairness in machine learning has primarily emphasized how to define,
quantify, and encourage “fair” outcomes. Less attention has been paid, however, to the …