Algorithmic bias has been a focus of research for many data scientist and machine learning researchers, but little research efforts have been dedicated to algorithmic bias in autonomous systems. The dynamic nature of autonomous systems makes it difficult to analyze for biases, hence we focus our research effort on the control loop of ML based autonomous systems. In this research, we adapted a machine learning based fairness algorithm designed for decision support systems in a real time and dynamic environment of an autonomous system. The final solution is in the form of a software module which provides access for auditing decision process of machine learning powered autonomous software systems. This, in turn, ensures fairness in the decision process of autonomous software systems.