In their talk entitled “In Planning We Trust”, Professor Karlien Vanderheyden of Vlerick Business School and Cristina Danila of Janssen Pharmaceutica conjectured that lack of trust can be caused by a combination of three aspects: human factors, such as users having unclear expectations of the system; machine factors, such as lack of transparency about what the system can do; and contextual factors arising from insufficient or poor management communication.

To eliminate these factors it’s clear we have to figure out how to let algorithms explain themselves in ways that humans can understand and appreciate. Professor Ann Vereecke of Vlerick Business School and Ghent University, and Kris Dockx of OMP, referred to the concept of XAI, or explainable artificial intelligence, as an answer to this challenge. According to Professor Vereecke, “Explainable AI consists of a set of tools and techniques that help humans not only to understand but also to interpret the recommendations that were made by the AI model.”

1. The first step is explaining how the algorithm works in theory. Because procedures that assign stock to demand can sound quite dry and boring, the explanation is made more engaging. This can be achieved by comparing it to something everyone finds way more interesting — in this case, online dating. And so, instead of assigning stock to demand based on abstract KPIs, Kris Dockx explains the algorithm through the device of assigning lonely men to single women by hair color preference.