Explaining inferences in Bayesian networks

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Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…Symeonidis and Manolopoulos, 2012; Pearl, 1988; Suermondt, 1992; Shafto and Goodman, 2008; Shafto, Goodman, and Griffiths, 2014; Sinha and Swearingen, 2002; Sørmo et al., 2005; Theis, Oord, and Bethge, 2015; Wang, Ruin, Velez-Doshi, Liu, Klampfl, and MacNeille, 2010a,b; Yap, Tan, and Pang, 2008).”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “Working backwards from the target node, EBI shows the derivation of each intermediate node and explains how missing and erroneous values are compensated by displaying these causal relationships in a DT hierarchy.”
Justification Narratives for Individual Classifications (2014, background): “While for many other models it is harder to define the values of these scores (in particular, the values in non-linear models may differ depending on the values of other features), there have been specific proposals for various models (Carrizosa et al., 2006; Yap et al., 2008) as well as proposals f”