Fairness in Machine Learning

Did you know that machine learning models are susceptible to biases and could result in unfair decision-making?

IEEE WIE Victorian section invited Assoc. Prof. Jeffrey Chan to talk more on this topic. The event was held via Zoom on 8th September with participants actively joining the discussion.

Event description: Machine learning is prevalent in many areas of today‚Äôs 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 introduced the thinking of bias, fairness and transparency from a computer science perspective and approaches to mitigate them.  He also discussed some of the projects in this space that they are doing within the Automated Decision Making + Society Centre of Excellence.

Speaker Bio: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.