Public Policy Analytics: Cases & Issues
Units: 6
This course looks closely at some practical issues that have emerged so far with the use oif predictive risk models in public sectors with a view to equipping students with the background and skills needed to successfully deploy predictive analytics when their turn comes. To do that, the course looks closely at practical cases with important lessons. Most of the cases reveal application of solutions which are technically sound but which meet hurdles of accountability, transparency, legitimacy and citizen empowerment.
For instance, how can an algorithm used to identify children at risk of mistreatment be hailed as a success in Pennsylvania, called “useless” in New Zealand and be the subject of successful legal action by citizens in Scotland? Are data techniques applied to the criminal justice systems prone to racial bias? What rights should citizens have over the dissemination and use of data collected on them by governments? What redress should citizens have when data anonymized by governments is re-identified?
These and other cases raise policy, ethical and management issues. In considering these issues, the course traverses the various levers available to governments such as legislation, administrative reform and participative democracy as well as the pitfalls.
All of these matters will be considered in a classroom that places a premium on student participation. This is a very new area and the course will work best if we take the journey together. The ultimate aim is to develop sound understandings of the principles which public policy professionals using big data analytic techniques can apply to achieve successful outcomes for both governments and their citizens.
Students completing this course will be able to:
Identify practical opportunities for successful use of data analytics in government service delivery
Nil