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We are pleased to deliver the following event through our (Virtual & Free) Seminar Series: Frontiers of Big Data, AI and Analytics.
Time: 15 April 2021 9:00AM - 10:30AM (Australian Eastern Standard Time (GMT+10)).
Discussion theme: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Speaker: Professor Cynthia Rudin (Duke University)
Abstract of talk With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice. Explanations for black box models are not reliable, and can be misleading. If we use interpretable machine learning models, they come with their own explanations, which are faithful to what the model actually computes. Cynthia will give several reasons why we should use interpretable models, the most compelling of which is that for high stakes decisions, interpretable models do not seem to lose accuracy over black boxes - in fact, the opposite is true, where when we understand what the models are doing, we can troubleshoot them to ultimately gain accuracy.
Structure of the event:
Talk given by a speaker (30 minutes)
A conversation with a discussant (35 minutes)
Q&A from audience (15 minutes)
Please see the link (early registration is encouraged to secure your seat)
Logistics: Zoom link will be provided 1 day prior to the event.
For more details, attached please find a flyer. We hope you find it beneficial for you.
Tomohiro, Robert and Valentin
Statistical Society of Australia
PO Box 213
Belconnen ACT 2616 Australia
02 6251 3647www.statsoc.org.auABN 82 853 491 081
Please direct enquiries to:
Marie-Louise Rankin, Executive Officer
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