Webinar on Fairness in Machine Learning
Did you know that machine learning models are susceptible to biases and could result in unfair decision-making?
Machine learning is prevalent in many areas of today’s society and increasingly a major component in the technology supporting our modern societies. In recent years, however, there has been growing awareness about how it is entrenching biases and inequality, as well as difficulty in understanding how it is coming up with certain decisions and outcomes.
In this talk, Assoc. Prof. Jeffrey Chan will introduce the thinking of bias, fairness and transparency from a computer science perspective and approaches to mitigate them. He will also discuss some of the projects in this space that they are doing within the Automated Decision Making + Society Centre of Excellence.
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- Date: 29 Sep 2022
- Time: 12:00 PM to 01:00 PM
- All times are (UTC+10:00) Canberra
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Speakers
Associate Professor Jeffrey Chan of RMIT University
Fairness in Machine Learning
Biography:
Jeffrey is an associate professor at RMIT University. His research has resulted in more than 100 peer-reviewed publications in the top conferences and journals in machine learning, recommendation, FAccT (Fairness, Accountability, Transparency), social network analysis, data driven optimisation and decision-making and interdisciplinary research that combines these fields to solve novel applications. Jeffrey is a chief investigator on multiple Australian Research Council (ARC) funded projects and partner investigator in the Automated Decision Making + Society Centre of Excellence. He has been funded by industry and the non-profit sector to work in data and machine learning driven, optimisation and decision-making applications including retail and marketing, sustainability, social media, energy and manufacturing.
Address:Melbourne, Victoria, Australia, 3000