Course description

This course deals with a number of issues in the philosophy of computation. The course is team-taught, and there are three 4-week modules, each with its own evaluation, completed by/at the end of the module.
The first module of the course (Dr. John Hacker-Wright) will cover the currently prominent theories of normative ethics in the context of issues in data science. Specifically, we will cover utilitarianism, Kantianism, contractualism, and virtue ethics. In each case, the theories will be introduced with examples that show some implications of the normative theory for ethical issues arising from emerging technologies. The second course module (Dr. Andrew Wayne) will focus on the implications of big data for scientific method, particularly in physics. Over the last fifteen years or so, computational methods have spread to almost every field of physics. In many fields of physics, traditional analytic and more recent computational methods exist side-by-side, with different research groups employing different methods to study the same physical system. This has prompted scientific debate about the merits of analytic versus computational methods. In this course module, we will look at this debate and assess computational methods with respect to prediction, theory confirmation and explanation. The third module (Dr. Samantha Brennan) will focus on social and political philosophy. We will look at issues of power, oppression, and privilege as they relate to data science. Drawing primarily on work in feminist philosophy, we’ll look at problems with big data related to race and gender as well as at the liberatory potential of data science.

Texts: 

To be provided

Evaluation: 

3 assignments of equal value, one for each module.