It is now well recognised across multiple disciplines and sectors that Automated Decision Making and Artificial Intelligence need to be built in a way that is fair, non-biased, and equitable. Yet, a major challenge in achieving fair, non-biased and equitable ADM/AI is a diversity of views and confusion about what these terms mean, how they are conceptualised and contextualised and how they might be assessed and measured.  This panel session advances the need for inter-disciplinary conversations in order to progress the design and use of ADM/AI. The panel focuses on the concepts of fairness, bias, and equity from different disciplinary perspectives, with some consideration of empirical research on the operationalisation and challenges within different sectors/contexts.  Following input from and dialogue with panellists with legal, computer science, policy, and social scientific perspectives of fairness, bias and equity, attendees will have the opportunity to work in small groups to develop a definitional delineation of these concepts and how they might be practically applied. 

Speakers: Associate Professor Jeffrey Chan, RMIT University 
Professor Paul Henman, University of Queensland (Host) 
Rakesh Kumar, University of New South Wales Professor 
Jackie Leach Scully, University of New South Wales 
Professor Jeannie Paterson, University of Melbourne 
Dr Emmanuelle Walkowiak, La Trobe University 
Dr Scarlet Wilcock, University of Sydney

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