Data-driven policy: AI in public policy and services

Improve decision-making through data-driven insights

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.

AI has huge potential in decision making

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.

What does TNO offer on Public policy and services?

• We design and guide experiments for public sector innovation and data-driven policy using our policy lab approach.
• We develop methodologies for multi-disciplinary collaboration and hybrid AI statistics.
• We critically evaluate the suitability of AI-based insights and applications in relation to specific societal challenges.
• We combine social science theories and insights with AI techniques and expertise.
• We work with policy makers on new frameworks for privacy, ethics and regulations to ensure responsible data policy.