Reasoning, Learning and Explainability are key to AI systems that must interact naturally to support users in decision making. Systems need to be capable of explaining their output. Regulations increasingly support users rights to fair and transparent processing in automated decision-making systems. Addressing this challenge is steadily becoming more urgent as the increasing reliance on learned models in deployed applications continues to be driven by the recent success of deep learning and other data-driven systems. Though models learned directly from data offer improved accuracy, mapping these concepts to facilitate human reasoning is difficult. In contrast, reasoning systems can offer transparency through logical alignment of representation and reasoning methods to allow the necessary insight into the decision-making process. This is a core principle behind explainability and is critical if we are to use AI with the intent of improving user performance and experience.
This workshop was only possible through the hard work and dedication of a number of individuals.
– Prof Nirmalie Wiratunga (Robert Gordon University) – Prof Leslie Smith (University of Stirling) – Prof Emma Hart (Edinburgh Napier University) – Mr Kyle Martin (Robert Gordon University) – Dr Sadiq Sani (Robert Gordon University)
Programme Committee – Dr David Corsar (Aberdeen University) – Dr Eyad Elyan (Robert Gordon University) – Dr Chenghua Lin (Aberdeen University) – Dr Stewart Massie (Robert Gordon University) – Dr Nir Oren (Aberdeen University) – Dr Andrei Petrovski (Robert Gordon University) – Miss Anjana Wijekoon (Robert Gordon University)
The organising committee would also like to thank all of our authors and attendees. Without you, this event could not have happened.