Information based explanation methods for deep learning agents -- with
  applications on large open-source chess models
Patrik Hammersborg Inga Strümke
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.