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This course focuses on the technical challenges in data science that societal and regulatory actions pose. It aims to introduce students the often very different and sometimes even conflicting perspectives from which policymakers and the technical community approaches these problems. We will review and discuss different examples from different areas of data science such as the extent to which machine learning and deep learning techniques conform with GDPR regulations on transparency, explainability, and accountability; impossibility theorems showing off the limits of data science methods; the mathematical foundations and data science techniques for mechanism design in order to manipulate beliefs (represented as transitive, anti-symmetric, and complete binary relations); and provide students as potential future product developers with the necessary knowledge to engage in responsible product development practices that are informed by regulatory requirements and expectations.
This course develops students' understanding of the role of data science in decision making and the impact of data science in the design of AI systems. The course reflects the main issues of controversy identified in international policy debates.
Subject to approval of the Head of Department of Mathematics and Statistics.