Understanding (and acknowledging) fallibility

Uncertainty: whether it be developing tools to help communicate it in lay-professional encounters, tools that take into account human uncertainty (and the consequent need for continuing change in a legal system), or methods to flag up / explain uncertainty to professional (or public policy) users of data science tools.


Understanding (and acknowledging) fallibility

This aim of this project is to better understand the impact of professionals’ (and public policy bodies’) growing appetite for ‘evidence-based’ decisions when this evidence stems from the exploitation of hereto unavailable data to build explanatory (or justificatory) models. This project will consider the extent to which there are ways of improving the public’s and professionals’ awareness of both the limitations inherent in the data that is relied on and the uncertainty inherent in the model built on the basis of this data. It could also consider the extent to which such awareness affects the way in which such decisions are at all challenged, and in what ways.

This particular PhD project lies at the intersection between public policy, statistics, computer science, law and philosophy. It requires a background in statistics and a strong interest in public policy and/ or professional ethics.

The successful candidate will have a first class undergraduate and Masters degree in any of the disciplines relevant to the above project (this includes applied mathematics, computer science, philosophy, law, medicine, psychology, public policy) together with demonstrable expertise in statistical / data science. The successful candidate also needs to demonstrate a genuine interest in professional ethics and/or public policy.