TNO research

At TNO I participate and lead several projects in the domain of human–AI interaction.

TNO research

Within TNO I work in the department of Human–Machine Teaming within the team Human–Agent/Robot Teaming. My research focuses on extending AI systems the functionalities they need to effectively and safely collaborate with all involved stakeholders. My focus is on AI systems that act as decision support systems (e.g., diagnostic support in healthcare), offer operator support (e.g., remote operators for autonomous sailing) or generally act as virtual agents (e.g., information gathering agents for the police). Aside from that, I work with physical autonomous systems and how they should perform their tasks and behave such that they fit in a team consisting of both humans and systems.

Below a list of my current projects and a few highlights of past projects. You can also take a look at my other activities overarching the projects such as my community projects, and my PhD research.

Current TNO projects

  • Explainable AI for decision support

    Research within TNO’s Appl.AI program on the requirements, interaction designs and technologies needed for an AI system to explain itself to various human stakeholders. Several AI use cases are tackled to explore and generalize the usefulness of explainable AI.

  • Explainable AI in smart energy systems

    A project on the potential of Explainable AI research in AI applications for consumers to manage their energy usage at home and energy suppliers to optimize the energy grid for households.

  • Iterative design of AI systems with moral models

    A project to develop a method that allows auditors and the general public to provide feedback on the moral behaviour of AI systems in a natural way with the help of conversational agent. This agent then aggregates the feedback and allows politicians and moral engineers to evaluate it, and subsequently implement it in a next iteration of the moral model.

  • Intelligent operator support system

    Within the European Horizon project MOSES, I lead the research on how AI-based support tools can be developed to support operators in supervising not one, but many autonomous operations in parallel in a safe and effective manner.

  • Delegation in human-AI teams

    Research on how humans can effectively and responsibly delegate autonomous systems and virtual agents in an efficient way that leverages the intelligences of these systems and agents as well as the unique human capabilities. Under my lead we develop the DASH concept.

  • Research roadmap on the operationalizations of AI and data science

    Together with other researchers from various domains this projects works towards a roadmap how to operationalize AI and data science. My role is as the lead on developing part of this roadmap on operationalizing the topic of human-machine teaming with colleagues from various companies and institutes.

Past projects

  • Research roadmap on Human-Machine Teaming

    A project to establish the second iteration of the roadmap on human-machine teaming research for the Dutch Department of Defense (DDoD). Within this roadmap we defined the research areas DDoD and their investment priorities to ensure their use of AI and autonomy is done effectively and in a responsible manner.

  • Actionable explanations through causal models

    Those who are subjected to an AI system’s decision need the ability to contest those decisions. This research resulted in a method that combines machine learning and causal models to generate explanations that support this ability by explaining what steps the subject can take to influence the AI system’s decision (e.g., change their lifestyle( or report their concerns effectively (e.g., report a biased decision or a violation of privacy).

  • Operator support on maritime vessels

    Current large maritime vessels have a sophisticated auto-pilot to follow a set course or to manage a position, even under extreme weather conditions. Within the project we developed a concept that allows a single operator to supervise this auto-pilot while roaming the vessel. This is made possible through predictive analytics such that the operator can be informed in time to return to the bridge to manage issues and deviations.

    Operator support video
  • Machine learning on EEG data for an improved virtual reality experience

    Latency is an issue for any virtual reality experience, where head movements are out of sync of what is projected. With a patented approach of EEG data and machine learning we managed to predict up to 0.5 seconds in the future if someone will move its head and in which direction.

How can humans collaborate better with AI systems?

  • Through Explainable AI!

    My PhD research contributes to a more responsible use of AI systems by providing humans the needed understanding how such systems make their decisions.

    My PhD research