Domain Knowledge-Aided Explainable Artificial Intelligence

Abstract

The lack of human-friendly explanations from Artificial Intelligence (AI)—based decisions is a major concern for high stake applications. Towards mitigating the concern, Explainable AI (XAI) is an emerging area of research. In pre-modeling explainability, which is one of the notions of explainability, one can introduce explainability before training the model. One can also introduce explainability during training of the model or after training the model, in general, both of which are known as post-hoc explainability. Unfortunately, post-hoc explainability is not readily transparent and can be misleading, as it explains after the decision has been made and can be optimized to placate a subjective demand which is a form of bias. As a result, the explanation from it can be misleading even though it seems plausible. Explainability should incorporate knowledge from different domains such as philosophy, psychology, and cognitive science, so that the explanation is not just based on the researcher’s intuition of what constitutes a good explanation. Domain knowledge is an abstract, fuzzy, and high-level concept over the problem domain. For instance, in an image classification problem, the domain knowledge could be a dog has four legs, or a zebra has stripes, etc. However, the use of domain knowledge for explainability is under-focused and bound to problem-specific requirements. This chapter focuses on the notion of a pre-modeling explainability of AI-based “black box” models using domain knowledge. We demonstrate the collection and application of domain knowledge along with the quantification of explainability on an intrusion detection problem. Although AI-based Intrusion Detection Systems (IDS) provide accelerated speeds in intrusion detection, the response is still at a human speed when there is a human in the loop. The lack of explainability of an AI-based model is a key reason for this bottleneck as a human analyst has to understand the prediction before making the final decision towards mitigation of a problem. To mitigate this issue, in this chapter, we incorporate the CIA principle (i.e., domain knowledge) in an AI-based black box model for better explainability and generalizability of the model and demonstrate the process in detail. A major portion of this chapter is a compilation of our previously published work [1,2,3,4]. We start with a brief discussion of the problem, network intrusion detection and prevention, that we use to demonstrate our approach. Then we briefly introduced relevant domain knowledge and how it can be integrated into the overall architecture. In Sect. 3, we describe our experiments, followed by Sect. 4 which contains a discussion on results from the experiments. We conclude with limitations and future work in Sect. 5.