Understanding the landscape of potential harms from algorithmic systems enables
practitioners to better anticipate consequences of the systems they build. It also supports the …

Data‐driven algorithms are widely used to make or assist decisions in sensitive domains,
including healthcare, social services, education, hiring, and criminal justice. In various …

As artificial intelligence (AI) technologies become increasingly powerful and prominent in
society, their misuse is a growing concern. In educational settings, AI technologies could be …

Firms use algorithms to make important business decisions. To date, the algorithmic
accountability literature has elided a fundamentally empirical question important to business …

In analyzing oppressive systems like racism, social theorists have articulated accounts of the
dynamic interaction and mutual dependence between psychological components, such as …

Momentum among practitioners has led to the development of practices to better identify and
minimize sociotechnical harms (eg,[22, 53, 122, 131]). Alongside broader movements …

In this chapter, I argue that morality might bear on belief in at least two conceptually distinct
ways. The first is that morality might bear on belief by bearing on ques-tions of justification …

The ethics of data and analytics, in many ways, is no different than any endeavor to find the
'right'answer. When a business chooses a supplier, funds a new product, or hires an …

In the science and values literature, scholars have shown how science is influenced and
shaped by values, often in opposition to the 'value free'ideal of science. In this paper, we aim …

Under what conditions does machine learning (ML) model opacity inhibit the possibility of
explaining and understanding phenomena? In this article, I argue that nonepistemic values …