Fairness, interpretability and diverse datasets are not enough – getting specific about algorithmic bias

Abstract

The concept of "biased data" is often too generic to be useful.  Through a series of cases studies, we will explore what algorithmic bias is, different types (with different causes), and debunk some common misconceptions.  We will cover why algorithmic bias is a problem worth addressing and some steps towards solutions.

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Host

Professor Shazia Sadiq

Shazia Sadiq is currently working in the School of Information Technology and Electrical Engineering at The University of Queensland, Brisbane, Australia. She is part of the Data and Knowledge Engineering (DKE) research group and is involved in teaching and research in databases and information systems. Shazia holds a PhD from The University of Queensland in Information Systems and a Masters degree in Computer Science from the Asian Institute of Technology, Bangkok, Thailand. Her main research interests are innovative solutions for Business Information Systems that span several areas including business process management, governance, risk and compliance, data quality management, workflow systems, and service science.

Speaker

Dr Rachel Thomas is co-founder of fast.ai, where she helped create the most popular free online course on deep learning, bringing more people around the world with diverse and non-traditional backgrounds into AI. She previously was founding director of the Center for Applied Data Ethics at the University of San Francisco, with a focus on issues of surveillance, disinformation, bias and justice in the tech industry. Rachel earned her PhD in mathematics at Duke University, and was an early data scientist and software engineer at Uber. She was selected by Forbes as one of 20 Incredible Women in AI and was profiled in the book Women Tech Founders on the Rise.