Machine Learning models are begin increasingly used within the industry such as by financial institutions, governments and commercial companies. In the past few years, there have been several incidents where these ML models show discriminatory behavior towards particular groups of people, leading to unfair decisions that can have negative impacts on the lives of these people. Therefore, eliminating bias and ensuring fairness within these models is crucial to the societal expectations these institutions would like to meet. While there are tools and research towards technical mitigation methods for bias and unfairness. There is a lack of focus on the process of implementing these tools and methods within the industry, to develop ML models with the consideration of fairness at an early stage. In particular, there is a lack of specification on what fairness goals and objectives the ML model should accomplish. Without having this clarity, industry stakeholders can apply the tools and algorithmic unfairness mitigation methods but if the fairness requirements are not defined, then a) one is still not sure whether the ML model is solving the correct fairness goals and b) one is still not sure what the trade-offs and feasibility within different fairness goals, and available resources may look like. To address this, we design a Multi-Level Fairness Framework (digital workflow) that aims towards supporting stakeholders within the industry to perform Requirements Engineering, specifically elicitation and modeling, for fairness in a Machine Learning Model.