Transparency is needed to increase the adoption rate of AI solutions in industry and to increase competitivity. By understanding the reasoning behind AI decisions, industries can identify and correct errors more effectively.
This is crucial in high-stakes environments where mistakes can have significant consequences. There are multiple methods available to create transparency, which can be used before or after the AI Model. They explain either global or very specific the way the AI algorithm has derived its conclusion.
We need to select the most appropriate XAI method based on the context and characteristics of the users. This
selection is not trivial work for data scientists. This white paper explores how XAI can revolutionise data collection, analytics, and visualisation, making AI more accessible and trustworthy.
This white paper has been written by Royal HaskoningDHV as part of the Engineering Business Intelligence (EBI) project.