Explaining B ayesian Networks in Natural Language: State of the Art and Challenges (2020)

Abstract: In order to increase trust in the usage of Bayesian Networks and to cement their role as a model which can aid in critical decision making, the challenge of explainability must be faced. Previous attempts at explaining Bayesian Networks have largely focused on graphical or visual aids. In this paper we aim to highlight the importance of a natural language approach to explanation and to discuss some of the previous and state of the art attempts of the textual explanation of Bayesian Networks. We outline several challenges that remain to be addressed in the generation and validation of natural language explanations of Bayesian Networks. This can serve as a reference for future work on natural language explanations of Bayesian Networks.

## introduction despite an increase in the usage of ai models in various domains, the reasoning behind the decisions of complex models may remain unclear to the end user. the inability to explain the reasoning taking of a model is a potential roadblock to their future usage @xcite . the model we discuss in this paper is the bayesian network (bn). a natural example of the need for explainability can be drawn from the use of diagnostic bns in the medical field. accuracy is, of course, highly important but explainability too would be crucial; the medical or other professional, for instance, should feel confident in the reasoning of the model and that the diagnosis provided is reliable, logical, comprehensible and consistent with the established knowledge in the domain and/or his/her experience or intuition. to achieve this level of trust, the inner workings of the bns must be explained. take for example the bn presented in @xcite for predicting the likelihood for coagulopathy in patients. to explain a prediction about coagulopathy based on some observed evidences, not only is the most significant evidence highlighted, but also how this evidence affects the probability of coagulopathy through unobserved variables. while a very useful tool to aid in reasoning or decision making, bns can be difficult to interpret or counter-intuitive in their raw form. unlike decision support methods such as decision trees and other discriminative models, we can reason in different directions and with different configurations of variable interactions. probabilistic priors and the interdependencies between variables are taken into account in the construction (or learning) of the network, making bns more suited to encapsulate a complex decision-making process @xcite . on the other hand, this linkage between variables can lead to complex and indirect relationships which impede interpretability. the chains of reasoning can be very long between nodes in the bn, leading to a lack of clarity about what information should be included in an explanation. with an automatic natural language generation (nlg) approach to explaining the knowledge represented and reasoning process followed in a bn, they can be more widely and correctly utilized. we will outline what information can be extracted from a bn and how this has been used to provide explanations in the past. it will be shown how this can be considered a question of content determination as part of an nlg pipeline, such as that discussed by @xcite , and highlight the state of the art in natural language explanation of bns. this is the first such review, to the best of our knowledge, that focuses on explaining bns in natural language. ## need for natural language explanation if generated textual explanations are written for a purpose and an audience, have a narrative structure and explicitly communicate uncertainty, they can be a useful aid in explaining ai systems @xcite . in early expert systems, explanation was considered a very important component of the system and textual explanations were identified as a solution for explaining reasoning to users (shortliffe and buchanan, 1984). textual explanation was also identified as important for the explanation of bayesian reasoning; @xcite claimed that textual explanation would not require the user to know anything about bns in order to interact with it effectively. many of the early textual explanations took the form of basic canned text and offered very stiff output. the developers of the early explanation tools for bns expressed a definite desire for a more natural language approach, rather than outputting numerical, probabilistic information, as well as facilities for interaction and dialog between user and system @xcite . the state of the art at the time did not allow for the creation of such capabilities for the system, and these challenges have still not been sufficiently revisited with the capability of the state of the art of today. figure 1 : example of explanation in legal domain from @xcite figure 1 contains an example of a potential natural language explanation that could be generated from a bn following the methodology in @xcite . this explanation attempts to pacify feelings of guilt in jurors. in the given example, members of a jury may feel regret after, having returned a verdict of not guilty, learning that the accused had prior convictions. by fixing "non-guilty verdict" and "prior convictions" as true in the network, the explanation aims to convince a juror that a defendant having prior convictions does not increase the probability of the existence of hard evidence supporting their guilt. while the clarity may suffer due to the explanation in present tense of events that have taken place in different timelines, this example is a marked improvement on past textual explanations of a bn. a narrative is created around the defendant and vague, natural language is used to create arguments to persuade the juror; much more convincing than the common approach of printing observations and probabilistic values. ## conclusion it is clear that in the 1990's and early 2000's, there was a desire for implementing an effective natural language explanation facility for bns. in many cases, the previous attempts were deemed unsatisfactory by their developers or evaluators, due to the fact that the state of the art at the time limited their ability to provide the kind of natural explanations that they wished. this paper highlights several challenges which should be revisited with state of the art nlg capabilities and with the improved ideas we now have of what should be provided in a satisfactory explanation.