Fairness of Machine Learning Algorithms for the Black Community

This paper seeks to study the limits of the definitions of algorithmic fairness in relation to a protected variable, namely skin color. Discrimination towards the black community have existed for a long time. ML algorithms have only amplified or revealed existing discrimination. AI is a mirror that reflects the reality of our societies. The lack of a universal definition of algorithmic fairness makes it difficult to detect cases of discrimination in machine learning algorithms. We believe that independent or sensitive variables such as skin color are benchmarks that could be used to decide whether or not a decision is fair. We also recommend avoiding the use of proxy data.