Extending MAP-independence for Bayesian network explainability

In the past years there has been an increasing interest in explainable AI (XAI), since
it can be a potential solution to the performance, ethical and legal concerns of the new
obscure complex models such as neural networks. Selecting transparent models over top
performing ones can be a better option in terms of both performance and explainability
[1]. As such, in this work we use Bayesian networks