Sarah L. Thomson
Bridging Explainable AI and Fitness Landscapes
Fitness landscapes are typically used to gain insight into algorithm search dynamics on optimisation problems; as such, it could be said that they explain algorithms and that they are a natural bridge between explainable AI (XAI) and evolutionary computation. Despite this, there is very little existing literature which utilises landscapes for XAI, or which applies XAI techniques to landscape analysis. In this talk, a recent paper in landscape analysis for neural architecture search is presented. This work considers the problem of channel configuration and leverages a tool called local optima networks to better understand the optimisation. The talk will conclude with suggestions for and discussions of possible future avenues for the intersection of explainable AI and fitness landscapes.
Biography:
Dr Sarah L. Thomson is a lecturer at Edinburgh Napier University, Scotland. Her PhD was in the fitness landscapes of evolutionary computation (EC). Since then, she has applied EC to problems in healthcare, aviation, agriculture, and logistics. She still has a passion for fundamental research and has continued to work in fitness landscapes since her PhD. More recently, she has branched into explainable AI and neural architecture search, and is particularly interested in combining evolutionary computing with machine learning and XAI.
ORCID: https://orcid.org/my-orcid?orcid=0000-0001-6971-7817
LinkedIn: www.linkedin.com/in/sarah-l-thomson-21a156135
Twitter/X: www.twitter.com/silverhaxt
