Beyond Responsible AI: 8 Steps to Auditable Artificial Intelligence

With novel artificial intelligence (AI) applications multiplying like rabbits these days, it may seem like the current wave of AI innovation is all beer and skittles. Lawsuits have a way of sobering up any metaphorical party and, in the wake of numerous high-profile racial bias and fairness cases, The Wall Street Journal reports that companies including Google, Twitter and Salesforce say they "plan to bulk up ethics teams responsible for evaluating the behavior of algorithms." In today's litigious environment, AI-powered business decisions must be more than explainable, ethical and responsible; we need Auditable AI. As the mainstream business world moves from the theoretical use of AI to production-scale decisioning, Auditable AI is essential because it encompasses more than the tenets of Responsible AI (AI that is robust, explainable, ethical and efficient). It's important to note that although the word "audit" has an after-the-fact connotation, Auditable AI emphasizes laying down (and using) a clearly prescribed record of work while the model is being built and before the model is put into production.