Ai for Egovernance: Combining Artificial Intelligence and Collective Intelligence to Develop Evidence-Based Ai Policy

This case study describes how New Jersey's AI Task Force combined artificial intelligence (AI) with collective intelligence (CI) to develop evidence-based policy recommendations. The study discusses the role of AI in addressing two chronic challenges in policymaking: information overload and a lack of meaningful public engagement. By deploying Policy Synth, an open source deep research toolkit that uses AI agents to conduct automated web research and generate policy solutions, alongside the All Our Ideas online engagement tool to collect input from over 2,200 workers, the Task Force saved time and worked more inclusively. The findings suggest that AI-enabled research tools can enhance efficiency and the democratic legitimacy of policy outcomes. The Task Force's recommendations have already led to tangible policy actions, including a statewide AI workforce training initiative and the development of an AI-powered labor market monitoring system. The study helps to advance our understanding of the opportunities AI presents to transform policymaking by making the process faster and more efficient, enabling more meaningful engagement with communities, and widening the breadth of evidence used to inform policy, while also highlighting critical risks, such as algorithmic bias, transparency concerns, and the risk of overreliance on AI-generated insights. It also offers lessons for institutions who wish to integrate AI into public governance while underscoring the need for ethical and accountable AI deployment in policymaking.