This seminar will examine two early public health interventions and their impact on the morals and ethics of the field: Elise A Mitchell will discuss the quarantining of slave ships, and Mathieu Corteel the assumptions surrounding 19th century health statistics. Details of the talks below.
While the failures of industrial-scale algorithms are often attributed to some failure of machine learning engineering, many of these failures actually stem from something else entirely: the human beings whose behavior generates the data used to build these algorithms. So the solutions to these algorithmic problems are as likely to require tools from behavioral economics as from computer science. For example, research shows that prejudice can arise not just from preferences and beliefs, but also from the way people choose. When people behave automatically, biases creep in: quick, snap decisions are typically more prejudiced than slow, deliberate ones, and can lead to behaviors that users themselves do not want or intend. As a result, algorithms trained on automatic behaviors can misunderstand the prejudice of users: the more automatic the behavior, the greater the error. We empirically test these ideas in a fully controlled randomized lab experiment, and find that more automatic behavi
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Five new scholars have joined the Society of Fellows in the Liberal Arts, an interdisciplinary community of postdoctoral fellows and Princeton faculty members with a great diversity of experiences and perspectives.