The Dark Side of Machine Learning Algorithms: How and Why They Can Leverage Bias, and What Can Be Done to Pursue Algorithmic Fairness

Machine learning and access to big data are revolutionizing the way many industries operate, providing analytics and automation to many aspects of real-world practical tasks that were previously thought to be necessarily manual. With the pervasiveness of artificial intelligence and machine learning over the past decade, and their epidemic spread in a variety of applications, algorithmic fairness has become a prominent open research problem. For instance, machine learning is used in courts to assess the probability that a defendant recommits a crime; in the medical domain to assist with diagnosis or predict predisposition to certain diseases; in social welfare systems; and autonomous vehicles. The decision making processes in these real-world applications have a direct effect on people's lives, and can cause harm to society if the machine learning algorithms deployed are not designed with considerations to fairness.