Risk and Decision-Making for Data Science and AI

Teacher: Norman Fenton

Content: This module provides a comprehensive overview
of the challenges of risk assessment, prediction
and decision-making covering public health and
medicine, the law, government strategy, transport
safety and consumer protection. Students will
learn how to see through much of the confusion
spoken about risk in public discourse, and will be
provided with methods and tools for improved risk
assessment that can be directly applied for
personal, group, and strategic decision-making.
The module also directly addresses the limitations
of big data and machine learning for solving
decision and risk problems. While classical
statistical techniques for risk assessment are
introduced (including hypothesis testing, p-values,
and regression) the module exposes the severe
limitations of these methods. In particular, it
focuses on the need for causal modelling of
problems and a Bayesian approach to probability
reasoning. Bayesian networks are used as a
unifying theme throughout.

Learning Aims and Outcomes:

  • understand the risk assessment challenges in public health and medicine, the law, finance, government strategy, transport safety and consumer protection
  • see through the many ways risk is misrepresented in the media and by different organisations
  • reason rationally about risk in a range of different contexts
  • understand the importance of trade-offs and utilities in risk assessment and decision-making
  • understand the importance of causal models for effective risk assessment, and to be able to build such models for personal, group, and strategic decision-making
  • be able to undertake decision-making that takes account of conflicting stakeholders and objectives
  • be able to understand and use basic probability and statistics (using appropriate tools) for risk assessment and quantitative decision-making.