Toward explainable artificial intelligence: A survey and overview on their intrinsic properties

Abstract

Artificial intelligence and its derivative technologies are not only playing a role in the fields of medicine, economy, policing, transportation, and natural science computing today but also in future industries such as electric vehicles and meta-universe. However, because of the black-box nature of most common artificial neural networks (DNNs), there needs to be more understanding of what is happening behind these astounding performances. The abstract reasoning process in the networks raises concerns about the security of AI systems. Because of this, more and more researchers are turning their attention to the explainability of black-box neural networks to find some attributions that explain the reasoning performed by the networks by studying the black-box nature of the networks. Deep neural networks and their explainability act as a mutually beneficial symbiosis, facilitating each other’s development. This survey reviews the deep network explainable methods applicable for the field of computing vision proposed within the last decade and categorizes these methods in terms of their starting point to explain deep neural networks. Focusing on their intrinsics, we review the methods that are applicable for object classification, object detection, and looked forward that of object tracking. In each cluster, we show that the methods share some similarities but have their own highlight. Furthermore, we shed light on the future development of the field of explainable AI.