Governance in silico: Experimental sandbox for policymaking over AI Agents

Paper by Denisa Reshef Keraa, Eilat Navonb and Galit Well: “The concept of ‘governance in silico’ summarizes and questions the various design and policy experiments with synthetic data and content in public policy, such as synthetic data simulations, AI agents, and digital twins. While it acknowledges the risks of AI-generated hallucinations, errors, and biases, often reflected in the parameters and weights of the ML models, it focuses on the prompts. Prompts enable stakeholder negotiation and representation of diverse agendas and perspectives that support experimental and inclusive policymaking. To explore the prompts’ engagement qualities, we conducted a pilot study on co-designing AI agents for negotiating contested aspects of the EU Artificial Intelligence Act (EU AI Act). The experiments highlight the value of an ‘exploratory sandbox’ approach, which fosters political agency through direct representation over AI agent simulations. We conclude that such ‘governance in silico’ exploratory approach enhances public consultation and engagement and presents a valuable alternative to the frequently overstated promises of evidence-based policy…(More)”.