Dear Colleagues/Friends,

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:

Introduction

Talk given by a speaker (30 minutes)

A conversation with a discussant (35 minutes)

Q&A from audience (15 minutes)

Closing remark

Registration:

Please see the link (early registration is encouraged to secure your seat)

Link: https://www.eventbrite.com.au/e/frontiers-of-big-data-ai-and-analytics-professor-cynthia-rudin-tickets-143860969031

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.

Best regards,

Tomohiro, Robert and Valentin