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Blog（https://citizens.is/blog/） · May 22, 2025 · Artificial Intelligence, Policy Synth, Collective Intelligence
From 2,200 Worker Voices to AI Policy: The New Jersey Model
When governments write policy about artificial intelligence, the people most affected by the change are often consulted late, lightly, or not at all. That is a serious problem when the subject is work.
Generative AI will not affect every worker in the same way. It may create new opportunities for some, automate tasks for others, change the value of skills, reshape management practices, and make familiar career paths less predictable. A useful public policy response needs evidence, but it also needs a clear signal from workers themselves.
New Jersey's AI Task Force offers one practical model for how to do that.
Established by Governor Phil Murphy in October 2023, the Task Force was asked to advise the state on artificial intelligence across safety, workforce, equity, literacy, and innovation. Its Workforce Training and Jobs of the Future Working Group faced a question that is becoming urgent everywhere: how should a state prepare workers for a labor market being changed by generative AI?
The answer was not to ask AI to decide. The answer was to combine public input, AI-assisted research, and human review.
Start With Workers
The working group first needed to understand what New Jersey workers were worried about. Using large language models, the team generated 96 concise challenge statements about the possible effects of AI on the state's economy and workforce. Those statements were not treated as answers. They became material for public prioritization.
Through the "AI and You"（https://innovation.nj.gov/ai-and-you/） engagement, workers used All Our Ideas to compare concerns and help build a ranked list. Over three weeks in August 2024, more than 2,200 private-sector workers participated. The result was not a traditional survey average or a stack of unstructured comments. It was a rank-ordered public signal about which AI workforce concerns felt most important to the people closest to the issue.
That matters because public engagement often fails at the handoff. Governments collect comments, publish a summary, and then move back into expert-only policy design. In New Jersey, the public ranking became an input into the next stage of policy development.
Use AI for the Research Work
The working group then used Policy Synth（https://arxiv.org/abs/2407.13960）, the open-source AI toolkit developed by Citizens Foundation and The GovLab, to turn the priority problems into possible solutions.
The process had two tracks. On one track, Policy Synth agents conducted large-scale automated research across online sources, academic journals, white papers, and other material to identify workforce risks and policy responses. The system generated 1,451 policy proposals across 20 issue areas.
On the second track, Policy Synth focused on the issues prioritized through worker input.