Dr Kaitlyn Vredenburgh

This course is compulsory on the BSc in Politics and Data Science. This course is available on the BSc in Philosophy and Economics, BSc in Philosophy, Logic and Scientific Method, BSc in Philosophy, Politics and Economics and BSc in Philosophy, Politics and Economics (with a Year Abroad). This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

This course introduces you to the core philosophy of science, philosophy of mind, and ethics concepts needed to build better technology and reason about its impact on the economy, civil society, and government.

Some questions that the course might consider include:

• What is intelligence, and how does it vary between types of agents (human, animal, artificial)? What are the normative assumptions behind research in intelligence?
• What is data, and how can we design more ethical data governance regimes?
• Can technology be racist? If so, what are promising strategies for promoting fairness mitigating algorithmic bias?
• Can we understand black box AI and explain its outputs? Why is it morally important that we do so?
• How can we embed human values into AI systems?

10 hours of lectures and 10 hours of classes in the WT.

Students will be expected to produce 1 piece of coursework in the WT.

Students will write a formative outline of how they would answer an essay question for the summative. Students will then respond to feedback on the outline in writing the summative. Students may also be asked to do class presentations or other activities in class, depending on the class teacher.

• Gabriel, “Towards a Theory of Justice for Artificial Intelligence”, Daedalus
• Friedman, Kahn, and Borning, “Value Sensitive Design and Information Systems”
• Serpico “What kind of kind is intelligence?”
• Henry Shevlin, Karina Vold, Matthew Crosby & Marta Halina, “The limits of machine intelligence”
• Halina, “Insightful artificial intelligence”
• Alexandrova and Fabian, “Democratizing Measurement: Or Why Thick Concepts Call for Coproduction”
• Northcott, “Big Data and Prediction: Four Case Studies”
• Simons and Alvarado, “Can we trust Big Data? Applying philosophy of science to software”
• Viljoen, “A Relational Theory of Data Governance”
• Johnson, “Are Algorithms Value Free?”
• Munton, “Beyond accuracy: Epistemic flaws with statistical generalizations.”
• Barocas, Hardt, and Narayanan, Fairness and Machine Learning: Limitations and Opportunities [selections]

Essay (70%, 2000 words) and essay (30%, 1000 words) in the ST.