Explainable Artificial Intelligence deals with how AI models can be explained and understood by humans to improve the interaction, usability and trust.

Within TNO, we work on two different aspects of explainable AI (XAI): the ‘technical’ explainability and the ‘communicative’ explainability. Technical explainability focuses on various techniques to offer insights into the inner working of (black box) algorithms, so that a human observer can understand how the machine has produced its outputs. TNO is using techniques such as contrastive explanations and counterfactual fairness to achieve this. Communicative explainability, on the other hand, focuses on the role of AI as a facilitator in transferring information to people, in a communicative process (like a dialog). It does not aim to explain AI itself, but to use AI to explain other phenomena. Conversational agents are one way to achieve this communicative aspect of AI.

Conversational Agents are being addressed in a growing number of TNO projects, for stakeholders like the Dutch National Police, insurance companies, and health and governmental organizations.


  • TNO offers different methods and tooling to provide insights into black box models
  • TNO offers knowledge on and implementations of conversational agents


  • Jasper van der Waa, Scientist Specialist, e-mail: