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The impact of computer science and related fields on our society and everyday life has increased tremendously, and computer scientists should therefore be aware of the societal and morally relevant impact of the artifacts they build and of the systems they contribute to. This awareness can be trained and sharpened. Furthermore, computer scientists ought to have the necessary competencies for making morally acceptable and professional decisions in the development processes they are participating in. The thoughts and insights of academic ethics - i.e. the field professionally concerned with ethics and morals - are a necessary precondition for these competencies, and they are a necessary counterpart to the technical understanding and competencies that computer scientists bring to design.
This course aims at bringing these two perspectives together. It will introduce relevant knowledge from the field of academic moral philosophy as well as soft skills needed to argue clearly, precisely, and convincingly (i.e. beyond the level of everyday discussions at bars and parties). You will learn how to apply these skills to problems that will surface at many points in your career - whether in research, industry, or other fields. The ideas will be explained, and the skills trained, through analyses of several currently much discussed fields and phenomena linked to technologies from data science and networked AI.
Ethical problems often present dilemmas and raise substantial controversy. In the seminar component of this course, you will evaluate state-of-the-art research papers and present them in ways that demonstrate your competencies and involve the audience in a meaningful controversy, showing how intellectually stimulating and fun a serious engagement with ethics can be.
This course covers:
- an introduction to the methods of philosophy, argumentation theory, and the basics of normative as well as applied ethics;
- an introduction to the schools of ethics that are commonly used to assess what is “right” or “wrong” in choices related to computational technology;
- the role of ethics in relation to other regulations such as law and industry self-regulation;
- starting points to evaluate practices and technologies already in use or not that far away, such as: surveillance and tracking (online, GPS, CCTV, …); big data, machine learning and predictive analytics and phenomena created by them, including filter bubble effects/echo chambers and bias/discrimination; autonomous vehicles;
- an overview of relevant choices that need to be made by data scientists, and principled approaches to making these choices based on ethical reasoning;
- selected design frameworks and techniques that are intended to “bake ethics into” systems, such as value-sensitive design or privacy by design;
- a guide through the multitude of ethics-related codes of professional associations;
- an outlook onto the question of whether machines themselves can be ethical agents.
