KETU 2265 - Responsible Use of Decision-Making Algorithms and AI in the Public Sector: a Case Study Approach

“Artificial intelligence” is a buzzword in the public sector, and algorithms are increasingly used to aid or make decisions. This course is suited for students who want to understand the issue and apply their takeaways to policy, advocacy, research or product management work.

The main questions addressed: when (not) to build automated decision-making systems? For and with whom? How to monitor their impact, especially with regards to inequities? How can governments remain accountable? What role does civil society play, and how can it push back against abuses?

The course’s starting point is an algorithm developed by the English government in 2020 to predict A-Level grades, and the backlash that ensued. Other cases will be studied to go deeper into specific aspects of designing and implementing algorithms.

An interest in digital technology in public policy and curiosity about the social and political implications of data policies and decision-making tools are essential;

A technical background is not necessary. Introductory resources will be provided for students unfamiliar with some of the class’ concepts.

No prior knowledge of AI policy or AI in government necessary.

Classes will be structured around:

-A group presentation of a real-life case study

-Interactive class discussion around a specific aspect of automated decision-making in the public sector, based on the readings, the A-level case study and the case study presented by the group that session

-A presentation by the lecturer, summarizing the insights of the class

External speakers (civil servants, researchers, members of civil society) will take part in some sessions to bring their point of view and share their experience on a topic, to illustrate the way algorithms can be implemented in an accountable way and/or effectively challenged.

50%: Group presentation: presentation of case studies: each group will document an existing algorithm, link it to one of the course’s topics, and present it to the class.

50%: Individual assessment (written assignment)

Students are expected to read 3-5 assigned sources and write short reflections before each class. The entirety of the reading list is provided at the beginning of the semester for students to organize accordingly.

Students are expected to read the case study ahead of the first class.