Information based explanation methods for deep learning agents -- with applications on large open-source chess models
Primary Area: visualization or interpretation of learned representations
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Keywords: Explainable AI, saliency maps, concept detection, large chess models, neural networks
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TL;DR: Select information based explanation methods applied to an open-source replacement of AlphaZero, in addition to the presentation of a novel method for creating saliency maps with strong guarantees wrt. information flow.
Abstract: With large chess-playing neural network models like AlphaZero contesting the state of the art within the world of computerised chess, two challenges present themselves: The question of how to explain the domain knowledge internalised by such models, and the problem that such models are not made openly available. This work presents the re-implementation of the concept detection methodology applied to AlphaZero in McGrath et al. (2022), by using large, open-source chess models with comparable performance. We obtain results similar to those achieved on AlphaZero, while relying solely on open-source resources. We also present a novel explainable AI (XAI) method, which is guaranteed to highlight exhaustively and exclusively the information used by the explained model. This method generates visual explanations tailored to domains characterised by discrete input spaces, as is the case for chess. Our presented method has the desirable property of controlling the information flow between any input vector and the given model, which in turn provides strict guarantees regarding what information is used by the trained model during inference. We demonstrate the viability of our method by applying it to standard 8x8 chess, using large open-source chess models.
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Submission Number: 1319
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