The primary goal of this paper is to provide a thorough overview of fairness and bias mitigation algorithms in machine learning and optimization while also looking at ethics in AI. This paper is a continuation of Fairness in ML: A Survey Part 1. The exploration digs into Fair Classification, Fair Clustering, and Fair Regression, exploring algorithms that contribute to ensuring fairness across different areas of machine learning and optimization processes. We also examine fairness measures used in optimization for reducing bias in protected classes, specifically concerning demographic data and equity in optimization problems. Looking at equity in optimization, we discuss the use of Gini coefficient, Hoover index, and McLoone index as social welfare functions. In addition we look at how data protection and user privacy are used in the creation of ethical algorithms.