This project seeks to develop a way to elicit and visualize bias in the hiring process through the use of Markov Decision Processes, a mathematical framework for modeling decision processes. Three forms of the simulation: User-defined, Random, and Q-learning, were created and their policies were analyzed and compared. Heat Map and Donut Pie visualizations are utilized to present the Policies created from the Models. This project is designed to display the decisions as a form of countering bias during the hiring process