Human-In-The-Loop Learning Methods Toward Safe Dl-Based Autonomous Systems: A Review

The involvement of humans during the training phase can play a crucial role in mitigating some safety issues of Deep learning (DL)-based autonomous systems. This paper reviews the main concepts and methods for human-in-the-loop learning as a first step towards the development of a framework for human-machine teaming through safe learning and anomaly prediction. The methods come with their own set of challenges such as the variation in the training data provided by the human and test-time distributions, the cost involved to keep the human in the loop during the long training phase and the imperfection of the human to deal with unforeseen circumstances and define safer policies.