A prominent company recently realized the machine-learning algorithm trained on its past hiring data had learned a bias against female candidates and so was unsuitable for resume evaluation. But given competing definitions of fairness, how should we decide what it means for an algorithm to be unbiased? Machine vision algorithms are systematically less likely to recognize faces of people of color. Since many face recognition algorithms are used for surveillance, would improving these algorithms promote justice? Deep fakes may pose serious challenges to democratic discourse, as faked videos of political leaders making incendiary statements cast doubt on the provenance of real videos. Do the researchers developing these algorithms, often academics funded by National Science Foundation grants, have an obligation to desist? In a field filled with such vexing questions, the ethical issue most commonly addressed by the media is whether a self-driving car should swerve to hit one person to avoid hitting two. In this class, we will go beyond the headlines to explore the ethics of technology. We will discuss issues such as transparency, bias and fairness, surveillance, automation and work, the politics of artifacts, the epistemology of deep fakes, and more. By the end of the course, students will be able to identify crucial moral and epistemic issues in contemporary technology and choose between solutions to these difficult problems.
Grading Basis
LG/SNC Elective Basis
Lecture - Required
Course Attributes
Phil. Thinking or Ethics
Pmathic Ctext: Ethical/Pol
HPS 1627
Academic Group
Dietrich Sch Arts and Sciences
Academic Organization
History & Philosophy of Sci
Pittsburgh Campus