" We need your help to train an AI to fix the Community Benefit Agreement problem."
What
happened: I've sat through four rounds of Community Benefit Agreement (CBA)
debates in my city, and each time the pattern repeats: heated arguments,
confused stakeholders, and agreements that ultimately fall short for residents
while doing little to inspire investor confidence either. That experience
convinced me the problem isn't just political, it's structural. And structural
problems can be approached systematically.
What
I'd like to see happen is a collaborative effort to train a dedicated AI agent
whose sole purpose is deconstructing, evaluating, and improving Community
Benefit Agreements — balancing the legitimate interests of investors with the
genuine needs of the cities where development takes place. The agent would have
five key assignments: (1) Break down existing CBAs clause by clause.
(2) Flag provisions that are vague, unenforceable, or inequitable.
(3) Benchmark agreements against comparable projects in other cities.
(4) Suggest language that protects community interests while remaining
realistic for developers. (5) Help negotiating parties get better
agreements faster.
Others
can help with contributions as simple as a story about a CBA that failed your
neighborhood, outcome data on what was promised versus what was delivered, or
subject-matter expertise in urban planning, real estate development, community
organizing, or municipal law. Every piece of it feeds the training and makes
the agent more effective in real-world negotiations. The goal is an AI reviewer
built on lived experience and verified data and not just theory.
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