Jon Garibaldi, University of Nottingham, UK; Alexander Gegov, University of Portsmouth,UK; Uzay Kaymak, Eindhoven University of Technology; Keeley Crockett, Manchester Metropolitan University, UK
IEEE Members: Free
Non-members: FreeLength: 00:59:22
06 Jun 2023
Jon Garibaldi, University of Nottingham, UK; Alexander Gegov, University of Portsmouth,UK; Uzay Kaymak, Eindhoven University of Technology; Keeley Crockett, Manchester Metropolitan University, UK; ABSTRACT: This panel will discuss a wide range of aspects of Explainable AI that may include informativeness, trustworthiness, fairness, transparency, causality, transferability, reliability, accessibility, privacy, safety, verifiability and accountability. The topics discussed at the panel will cover aspects of Explainable AI that may include local and global scope, specific and agnostic models, as well as aspects of constructive, what-if, counterfactual and example-based explanations. Other potential topics may include recent developments related to real world bias of AI, how this bias is reflected in data bias, the encoding of data bias in algorithmic bias, its uncovering by Explainable AI, and how the latter can be used for closing the loop by mitigating real world bias of AI. The panel will also explore current challenges and future perspectives in Explainable AI that may include formalisation and evaluation of explanations, their adoption in industry, their potential for improving human machine collaboration and their ability to facilitate collective intelligence, responsibility, security and causality in AI.