Artificial Intelligence (AI) made leapfrogs of development and saw broader adoption across industry verticals when it introduced machine learning (ML). ML helps in learning the behavior of an entity using patterns detection and interpretation methods. However, despite its unlimited potential, the conundrum lies in how machine learning algorithms arrive at a decision in the first place. Questions like, “What are the processes they adopted, and at what speed? How did they make such autonomous decision?” often raises concern about reliability on ML models. Though it helps in parsing huge amounts of data into intelligent insights for applications ranging from fraud detection to weather forecasting, the human mind is constantly baffled how it achieves conclusions. Moreover, the recurrent need to comprehend the procedures behind the decisions becomes more crucial when there is a possibility that the ML model makes decisions based on incomplete, error-prone, or one-sided (biased) information that can put few gatherings inside the network at a disadvantage. Enter Explainable AI (XAI).
While AI and ML have brought a digital revolution, the concerns rise on how these systems arrive at a conclusion or decision. XAI may help address the black box of AI, ML, DNN, and other complex neural networks.