Understanding ML bias and explainability

One of the key focus areas for ML governance is bias detection and model explainability. Having ML models exhibiting biased behaviors not only subjects an organization to potential legal consequences but could also result in a public relations nightmare. Specific laws and regulations, such as Equal Credit Opportunity Act, prohibit discrimination in business transactions, such as credit transactions based on race, color, religion, sex, nationality origin, marital status, and age. Some other examples of laws against discrimination include the Civil Rights Act of 1964 and Age Discrimination in Employment Act of 1967.

ML bias can result from the underlying prejudice in data. Since ML models are trained using data, if the data contains bias, then the trained model will also exhibit biased behaviors. For example, if you build an ML model to predict a loan default rate as part of the loan application review process, and you use race as one of the...