XAI and Bias of Deep Graph Networks
Michele Fontanesi, Alessio Micheli, Marco Podda · 2024
Generalization in machine learning involves introducing inductive biases that restrict the solution space of the learning problem, allowing for the inductive leap.In this paper, we show the existence of different inductive biases between convolutional and recursive Deep Graph Networks (DGN) by applying Explainable AI (XAI) methods as model inspection techniques.We show that different architectures can perfectly solve the given tasks by learning different labelling policies.Our results promote the usage of different architectures to address a task and raise warnings on the assessment of XAI techniques as their benchmarks may contain more ground truths than those provided.* Research partly funded by PNRR -M4C2 -Investimento 1.3, Partenariato Esteso PE00000013 -"FAIR -Future Artificial Intelligence Research" -Spoke 1 "Human-centered AI", funded by the European Commission under the NextGeneration EU programme.