Session

Panel: Tackling Bias in AI

Artificial Intelligence has seen immense advancements in the 21st century, and its impact has been described as “the next electricity”. Yet, even the most superlative AI algorithms are only as good as the underlying training data – and this leads to bias in the algorithmic decisions. In this panel, our experts will discuss how deep and challenging are the actual biases in AI algorithms. Can we trust the algorithms deployed today (in medicine, finance, security, legal, HR) to accurately model and protect the interests of everyone. And if not, what techniques can be used to quantify and then eliminate those biases.

Biography

Noel Sharkey PhD, DSc FIET, FBCS CITP FRIN FRSA is Professor of AI and Robotics and Professor of Public Engagement at the University of Sheffield and was an EPSRC Senior Media Fellow (2004-2010). He has held a number of research and teaching positions in the UK (Essex, Exeter, Sheffield) and the USA (Yale,and Stanford). Noel has moved freely across academic disciplines, lecturing in departments of engineering, philosophy, psychology, cognitive science, linguistics, artificial intelligence and computer science. He holds a Doctorate in Experimental Psychology and a Doctorate of Science. He is a chartered electrical engineer, a chartered information technology professional and is a member of both the Experimental Psychology Society and Equity (the actor’s union).

He has published well over a hundred academic articles and books as well writing for national newspaper and magazines. In addition to editing several journal special issues on modern robotics, Noel has been Editor-in-Chief of the journal Connection Science for 22 years and an editor of both Robotics and Autonomous Systems and Artificial Intelligence Review. His research interests include Biologically Inspired Robotics, Cognitive Processes, History of Automata/Robots (from ancient to modern), Human-Robot interaction and communication, representations of language and emotion and neural computing/machine learning. But his current research passion is for the ethics of robot applications.

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