Keynote Speaker

Topic: Explainable AI is dead! Long live Explainable AI!

Abstract: In this talk, I argue that we need to re-frame how we conceptualise explainable decision support. Recent research shows that the current paradigm of giving a recommendation/prediction and explaining it does not really improve human decision making, with some exceptions. I argue that this is because we have failed to account for two things. First, recommendations (and their explanations) take control from human decision makers, limiting their agency. Second, giving recommendations and explanations does not align with the cognitive processes employed by people making decisions. I propose a machine-in-the-loop conceptualisation of decision support, which I call Evaluative AI, in which machine learning and other AI models are used to find evidence to help confirm or deny the hypotheses of decision makers, rather than provide a decision. This approach of ‘decision support as supporting the decision making process’ is different from ‘decision support as proposing decisions’, but the technical tools required for it look a lot like current XAI tools.

Bio: Tim Miller is professor of artificial intelligence in the School of Electrical Engineering and Computer Science at The University of Queensland, Brisbane, Australia. His primary area of expertise is in artificial intelligence, with particular emphasis on: human-AI interaction; AI-assisted decision support; explainable AI (XAI); and reasoning about action and knowledge. His work is at the intersection of artificial intelligence, interaction design, and cognitive science/psychology, bringing in models of decision making and interaction from other fields to build more human-centred AI systems. His applied work has been in several areas including defence, health, and finance. Prior to his appointment at The University of Queensland, he was a professor of computer science at The University of Melbourne, where he was founding co-director of The Centre for AI and Digital Ethics.  

Homepage: https://uqtmiller.github.io/