The rapid rise in machine learning applications in criminal justice, hiring, healthcare, and social service intentions substantially impacts society. These wide applications have heightened concerns about their potential functioning amongst Machine Learning and Artificial Intelligence researchers. New methods and established theoretical bounds have been developed to improve the performance of ML systems. With such progress, it becomes necessary to understand how these methods and bounds translate into policy decisions and impact society. The researchers continue to thrive for impartial and precise models that can be used in diverse domains.