Dr. ir. Anne Fleur van Veenstra
- Policy Lab
- Data-driven policy
- Digital governance
- Public sector innovation
Every day, policy makers and public servants face complex challenges. Some of these challenges are: fighting long-term unemployment, meeting sustainability goals and anticipating technological impact. These policy makers and public servants are expected to develop effective, timely and preventive interventions to address these issues. Together with governmental agencies, TNO explores ways of using Artificial Intelligence to support policy development and decision making processes with data-driven insights.
Public policy and services
Decision-making can be hugely improved through data-driven insights. Providing new data-driven insights will help in identifying societal threats, risks and opportunities at an early stage. It can also improve how we use existing data. Governments typically have access to a rich set of data. Think of data on residential energy usage, income and media usage. AI can also be used to search and analyze large bodies of public documents such as case law, permits and scientific reports to support and improve fair decision making processes.
Furthermore, AI techniques, such as natural language processing and image recognition, can be used to identify patterns in society and in individual behaviour. The use of AI to obtain new insights and identify societal threats, risks and opportunities at an early stage can support effective public interventions.
The potential of AI supported decision making in the area of public policy and services, is still largely uncharted territory. This is partly due to the fragmentation of data sources over different stakeholders, incomplete data sources in some domains, and the lack of explainable methodologies.
Questions that are currently being asked in this area include: How can AI-enabled data-driven decision-making be applied to complex social issues? Which relevant methodologies need to be developed? How can the results of analyses be scaled up? How can we apply domain expertise in the system’s design? How do we avoid biases and unwanted feedback loops, and how do you validate outcomes and ensure transparency? These questions need to be addressed in order to properly use AI in public policy and services.