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Data Governance and Policymaking


Admission requirements

This course is only available to Honours students. The maximum number of students is 25-30.


This course is designed to teach students about the most important and controversial Big Data applications currently being used in the public sector. The course has a practical and utilitarian component, which is to introduce students to what these applications are. It also has an analytical component, which is to learn and apply critical thinking towards such applications using findings from scholarly literature and public discussion about real cases.

On the first component, it will address questions of how the technologies work and why governments use them. For example, students will be introduced to the basic working principles of predictive policing, fraud detection, and smart public installations such as lighting and traffic systems. These are each different in important ways and there are different technical and social consequences. Students will learn about key technology ‘affordances’ and frameworks that can help know what to highlight and be critical of.

A second component will involve learning about real world cases and the arguments and evidence available from academic research. This component will pose questions such as what ethical issues arise from use of the technologies, whether robots will decide all the important matters that affect our lives, and whether there is anything that governments and citizens can do to use technologies to make government better. For example, we may review and discuss cases of Google’s failed smart city project in Toronto, the Dutch fall-out from the ‘Toeslagenaffaire’, or Rio de Janeiro’s policing of favelas. The global spread of big data technologies will be used as an opportunity to learn about a wide variety of countries and cities.

Students will learn to navigate the complex technical and ethical issues and put forward their own arguments through discussion and debate. Together, these skills and knowledge will help students to critically assess these tools and to become more expert both as critical users of technologies and as systems architects.

Course objectives

  • Describe examples of different kinds of Big Data applications in public services

  • Debate the public values pros and cons of artificial intelligence in the public sector using real world examples and academic literature

  • Carry out a research project that critically evaluates an algorithmic decision-making system from the perspective of protecting and improving public values.

Research – Analysis – Problem Solving – Project Work – Digital Skills – Cooperation – Communicate verbal – communicate in writing – presenting –resilience.


On the right side of programme front page of the studyguide you will find links to the website and timetables, uSis and Brightspace.

Mode of instruction

There will be a total of 7 class meetings.
14 contact hours and 126 hours of self-study and groupwork

Assessment method

1. Class participation (25%)
2. Assignment #1: Policy memo (individual) – Students will produce a brief (2-page, single-spaced) memorandum analyzing a data-driven policy or program. (25%)
3. Assignment #2: Analysis briefing (indvidual) – Students will give a succinct verbal presentation of the analysis provided in their policy memo. (10%)
4. Assignment #3: Research report (group) – Working in small teams, students will prepare a report (approx. 4000 words, excluding references) critically analyzing an existing or proposed data-driven public program or policy (40%)

All assignments must be sufficient in order to succeed for the course

Reading list



Brightspace will be used in this course. Registration for uSis and Brightspace will be done centrally.
TGC coordinator/administration will take care of enrollment.


Teachers: Alex Ingrams Matthew Young

Coordinators: Alex Ingrams, Matthew Young


Data science, governance, public policy, big data