Artificial intelligence (AI) has achieved growing momentum in its application in many fields to deal with the increased complexity, scalability, and automation, and that also permeates into digital networks today. A rapid surge in the complexity and sophistication of AI-powered systems has evolved to such an extent that humans do not understand the complex mechanisms by which AI systems work or how they make certain decisions -- something that is particularly a challenge when AI-based systems compute outputs that are unexpected or seemingly unpredictable. This especially holds true for opaque decision- making systems, such as those using deep neural networks (DNNs), which are considered complex black box models. The inability for humans to see inside black boxes can result in AI adoption (and even its further development) being hindered, which is why growing levels of autonomy, complexity, and ambiguity in AI methods continues to increase the need for interpretability, transparency, understandability, and explainability of AI products/outputs (such as predictions, decisions, actions, and recommendations). These elements are crucial to ensuring that humans can understand and -- consequently -- trust AI-based systems (Mujumdar, et al., 2020). Explainable artificial intelligence (XAI) refers to methods and techniques that produce accurate, explainable models of why and how an AI algorithm arrives at a specific decision so that AI solution results can be understood by humans (Barredo Arrieta, et al., 2020).
Apr-27-2021, 07:14:15 GMT