As the use of AI continues to permeate the business world and influence decision making, there will be increasing scrutiny and attention given to interpretability of AI in order to support organizational adoption of AI solutions. With this, we will see an increase in legal and technical experts focusing on how to effectively audit AI algorithms for bias. Human-interpretable models that account for biases such as gender or race will help prevent occurrences such as the recent gender-based Apple Credit Card algorithmic misstep