One of the most unfortunate things about the current Artificial Intelligence (AI) resurgence is the promotion of technology that make black boxes out of such AI based systems. In strange ways, we are drawn by this “unexplainable” way in which these intelligent systems make decisions of whether it is a picture of a cat or a dog. But in a business context we not only expect, but lawfully require, business decisions to be explainable (Explainable AI). Why was this loan rejected? Why have you prescribed this medication?

Building AI applications can be hard. Building automated business decision applications can also be hard, but using the Decision Model and Notation (DMN) is making it dead simple to build explainable business decision systems by allowing simple visual modeling of decision requirement and decision logic. Come and learn how you too can build intelligent decision systems that can explain their answers.

Learning Objectives:

• Overview of the current AI resurgence
• What are the strengths and weaknesses of symbolic and non-symbolic AI
• How to visually model Business Decisions using DMN
• Adding predictive analytics to your explainable business decisions