Bias, Fairness, and Accountability with AI and ML Algorithms

Artificial intelligence (AI) techniques are used increasingly in many areas of applications, including banking and finance. They have several advantages over traditional statistical methods: i) ability to handle new data types such as text, audio, and images; ii) flexible models that yield excellent predictive performance; and iii) ability to automate many of the routine, and time-consuming, tasks in model development. However, the use of these algorithms also raise several challenges. A well-known problem is the opaqueness of ML models and the difficulties in understanding and interpreting the model results. In this paper, we focus on a related and equally important challenge: potential for bias and lack of fairness when using AI/ML techniques.