New AI Lab for effective and responsible oversight
Collaborating on a new generation of responsible AI is the goal of the ICAI Lab AI4Oversight, launched on October 19. In partnership with four collaborators and two universities, the lab aims not only to develop responsible and explainable AI but also to optimize collaboration between inspectors and AI systems. Scarce inspectors will be maximally supported with new algorithms and methodologies, enabling a more effective use of their time.
The ICAI Lab AI4Oversight officially started on October 19th and includes:
- 5 partners: Human Environment and Transport Inspectorate (ILT), Netherlands Labour Authority, Inspectorate of Education, Netherlands Food and Consumer Product Safety Authority and TNO.
- 2 universities: Utrecht University and Leiden University
Optimizing Inspection Deployment: The Key Role of AI
Supervision does not mean inspecting everything everywhere continuously; that's impossible. Choices must be made. The challenge is to inspect precisely where the societal contribution is greatest.
How do you achieve a risk-based approach, deploying inspectors as effectively as possible at the right times and places? This is the task for which regulatory bodies are collectively seeking a solution. The use of artificial intelligence (AI) plays a significant role, especially as these become more sophisticated.
"We already use AI where possible for a responsible, selective, and effective deployment of our inspectors. But there are more opportunities ahead," summarizes Mattheus Wassenaar, Inspector General of ILT, the motivation to start this collaboration.
"Together with universities, we will develop methods to ensure that our people are optimally supported by algorithms. Inspectors are scarce, and they don't generate much data. This means we need algorithms that learn faster with limited data. There is also a focus on preventing unwanted selection bias. We are doing everything to collectively develop AI that can be deployed in the oversight domain responsibly and reliably."
Developing and Testing New Methods
Practical experiences have highlighted the need for new methods in AI within the oversight domain. This led to the current research agenda, where universities are developing methods that align well with practice. All this is done in close collaboration with participating inspectorates.
In this way the gap is bridged between theory and practice, where inspectorates and TNO can use the new methods, and universities can build their research on practical case studies. The research agenda focuses on the topics: collaboration between humans and machines, faster and fairer learning algorithms, and the contribution of AI to behavior improvement.