Impact of Explainable AI and Task Complexity on Human-Machine Symbiosis

Artificial Intelligence (AI) helps humans perform faster and better with accuracy. The human-machine interaction becomes effective when both human and machine understands each other. This interplay of human and machine is called human-machine symbiosis that combines the best of both entities. Automated systems such as AI predicts outcomes without any explanation. A tool that makes this black box into a white box is the explainable AI (XAI). XAI provides results with an explanation to the decision makers in a humanly understandable way. For an effective human-machine symbiosis, another important factor is decision task complexity. The extant literature is still silent on explaining how the interplay of XAI techniques and decision task complexity impacts decision maker\u27s perception of the human-machine symbiosis. Therefore, in this research, we are investigating the impact of XAI and decision task complexity on perceived human-machine symbiosis. Using information overload and algorithmic transparency theories, in this research, we develop a causal model to explain the relationships