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.

Shapash is a Python library released by MAIF data team in January 2021 to make Machine Learning models understandable by everyone. Shapash is currently using a Shap backend to compute local contributions. You will find the general presentation of Shapash in this article. Version 1.3.2 is now available and Shapash allows the Data Scientist to document each model he releases into production. Within a few lines of code, he can include in an HTML report all the information about his model (and its associated performance), the data he uses, his learning strategy, … this report is designed to be easily shared with a Data Protection Officer, an internal audit department, a risk control department, a compliance department or anyone who wants to understand his work.

Today's AI systems make weighty decisions regarding loans, medical diagnoses, parole, and more. They're also opaque systems, which makes them susceptible to bias. In the absence of transparency, we will never know why a 41-year-old white male and an 18-year-old black woman who commit similar crimes are assessed as "low risk" versus "high risk" by AI software. Oren Etzioni is CEO of the Allen Institute for Artificial Intelligence and a professor in the Allen School of Computer Science at the University of Washington. Tianhui Michael Li is founder and president of Pragmatic Data, a data science and AI training company.