Ihaka Lecture Series 2020

2020 series – The role of statistics and computing in public and social policy

https://www.auckland.ac.nz/en/science/about-the-faculty/department-of-statistics/ihaka-lecture-series.html

Corporations are collecting and mining mountains of data to make better consumers of us all, but there are also vast quantities of data being gathered by public organisations for administrative and policy purposes.

The 2020 Ihaka Lecture Series brings together three experts to discuss the challenges and rewards of applying data science to societal issues.

Lecture 1 | Monday 9 March 2020

The triumph of the quants?: Model-based poll aggregation for election forecasting

Professor Simon Jackman, Chief Executive Officer at the United States Studies Centre, will examine recent successes and failures of predictive models of election outcomes. Professor Jackman will also discuss trends and discontinuities in the evolution of public opinion over election campaigns, spatial smoothing and pollster biases.

Professor Jackman’s current research focuses on opportunities and challenges of web-based survey research, consequences of under-representation in social research, and developing methodologies for assessing symmetry and fairness.

Time: 6.30pm
Location: Lecture Theatre PLT1, Ground Floor, Building 303, 38 Princes Street, Auckland

Lecture 2 | 18 March 2020

Machine learning for causal inference: Magic elixir or fool’s gold?

Professor Jennifer Hill from New York University will review the conceptual issues involved in understanding causal mechanisms and describe the potential for machine learning to improve our understanding of these mechanisms.

Professor Hill develops and evaluates methods that help us answer the causal questions that are vital to policy research and scientific development. Methodologically, she focuses on methods for situations where it is difficult or impossible to perform traditional randomized experiments. In her applied work, Professor Hill focuses on applications of randomized experiments to policy and practice and on using machine learning to generate causal hypotheses.

Time: 6.30pm
Location: Lecture Theatre PLT1, Ground Floor, Building 303, 38 Princes Street, Auckland

Lecture 3 | 25 March 2020

Implementing a machine learning tool to support high-stake decisions in child welfare: A case study in human centred AI

Professor Rhema Vaithianathan, from the Centre for Social Data Analytics at AUT, will reflect on what we can learn about applying data analytics in a trusted way, covering key concepts like consent, transparency, fairness and community voice, and how they can contribute to project success or failure.

Professor Vaithianathan will talk about emerging ‘rules of engagement’ for social good uses of data analytics, drawing on her experience implementing the Allegheny Family Screening Tool, a machine learning tool used to support screening of child abuse calls in Allegheny County, PA, United States since 2016.

Time: 6.30pm
Location: Lecture Theatre PLT1, Ground Floor, Building 303, 38 Princes Street, Auckland