We the People: Individual Rights Are the New Metric for Fair AI
When we started writing AI for Community, I wasn’t in Washington D.C. I was sitting in a cafeteria at Florida Gulf Coast University, talking with students about what a fair use of AI might look like in their communities. I was reading about engineers at NVIDIA collaborating with Māori leaders to preserve Indigenous language through machine learning. I was building a prototype AI to honor my late mother’s legacy, capturing her voice, her scholarship, her way of seeing the world.
None of these projects began with code. They began with care. With context. With the question that kept surfacing everywhere: Does AI reflect us, or just the people who made it?
Today, we have a new roadmap. And like the artwork featured throughout this piece, reflections of what it means for AI to carry memory, meaning, and dignity, it reminds us that the future of AI isn’t just technical or political. It’s about serving people, transparently, fairly, and with the full weight of public accountability.
The U.S. government released the America’s AI Action Plan, a sweeping strategy that treats AI as public infrastructure, as foundational as roads, schools, or the electrical grid. But unlike those earlier systems, AI is being shaped in real time. Communities now have a chance to help build it, not just respond to it.
AI is showing up everywhere: in classrooms, clinics, local courts, and newsrooms. It’s part of the daily systems that shape opportunity, access, and fairness. That’s why this moment matters: because the communities who rely on these systems should help shape them, too.
The new federal AI Action Plan reflects that shift. It presents not just a vision for technology, but a commitment to shared governance, innovation, and accountability. It signals something bigger than digital progress: a call for civic engagement. The AI future is no longer someone else’s problem. It’s ours to shape.
AI Infrastructure Needs Public Participation
In the past decade, I’ve worked at the intersection of policy, culture, and technology. At the White House Presidential Innovation Fellowship Program, I helped lead a major AI for Health Tech Sprint, a cross-agency collaboration that was later cited in both the National AI R&D Strategic Plan and in White House briefings as a model for health innovation.
I’ve worked globally through storytelling and fieldwork, documenting how knowledge, culture, and environment intersect. And I co-authored AI for Community, that was just launched at the London AI Summit, a guide rooted in one belief: AI should reflect lived experience, not just code.
When we published that guide, communities were getting ready. But the tools weren’t — as platforms prioritized monetization over civic value.
Now, these tools won’t just be available, they’ll be required. The America’s AI Action Plan mandates the use of open-weight and evaluable models across critical domains. It’s about creating scientifically grounded systems that can be measured, stress-tested, and governed by evidence. These tools will no longer be optional.
Open systems don’t just enable access. They create accountability. They become the guardrails that make AI transparent, safe, and context-aware. Because when AI becomes infrastructure, it has to reflect the people it serves. Fairness isn’t just abstract anymore. Individual rights are the new metric.
From Coast to Community: A Plan for All Americans
The Action Plan lays out several major initiatives:
It’s a bold blueprint. But ambition alone isn’t enough. The challenge now is turning commitments into enforceable standards, and that includes protections for all Americans.
From Bias to Rights: A Stronger Foundation
The plan notably distances itself from prior federal frameworks on diversity, equity, and inclusion (DEI). It removes this language from key guidance documents, signaling a shift in political vernacular. While we may no longer use the vocabulary of bias or inclusion, that doesn’t mean we abandon the responsibility to ensure AI systems are just, accurate, and safe.
What will now be codified into law is a requirement for scientific rigor, applied with precision. The plan prioritizes objective evaluation and testing. This isn’t about replacing one term with another. It’s about ensuring that the systems we build are rooted in measurable fairness. Rights-based AI is a legal and technical framework to measure, audit, and protect Americans. That’s why scientific rigor is more essential than ever.
The plan emphasizes data quality, model interpretability, and the development of evaluation ecosystems. These tools are key to measuring and mitigating harm, ensuring systems perform as intended, and maintaining public trust.
This shift is important. It moves the conversation from vague trust to auditable trust. From calling out harm to preventing it scientifically. From focusing on statistical bias to protecting individual rights and civil liberties.
It’s important to emphasize that datasets must reflect the real experiences of U.S. communities. Without that, no model can serve everyone equally. And this is where open systems shine: when researchers and local leaders, can inspect and test AI, they can improve it. That’s the essence of accountability.
Open-Source vs Open-Weight: Why It Matters
In discussions of transparent AI, it’s essential to distinguish between “open-source” and “open-weight” models:
Open-source AI means full transparency: access to code, architecture, weights, and sometimes training data. It enables complete reproduction, inspection, and adaptation.
Open-weight AI means you can use or fine-tune a model’s trained weights, but may face limits on licensing, data access, or retraining rights.
Knowing these differences matters. Communities need to understand not just what models do, but what they’re allowed to do with them. That’s the foundation of community access.
Building Civic Infrastructure, Not Just Tech
Across the country, communities can build local AI systems that reflect their context:
In Alaska, rural planners can train predictive models on transportation delays and seasonal supply chains.
In Alabama, radio networks can explore AI-powered chatbots tuned to regional dialects and cultural references for health outreach and civic education.
In Austin, nonprofits can embed AI into media workflows to decode housing policy and summarize public meetings, ensuring transparency and public engagement.
When communities lead, AI becomes infrastructure with them, not over them.
Cultural Intelligence = Practical Intelligence
AI that works well doesn’t just understand data. It understands people. Culture. History. Trust. That’s why AI for Community emphasized cultural intelligence as a technical requirement.
Systems that ignore local nuance fail. Systems that adapt earn trust. The Action Plan recognizes this, by prioritizing flexibility and rights-based evaluation. That’s where the next phase of work lies: embedding enforceable protections and ensuring representation.
A Positive Vision — If We Build It Together
If we want AI to work for all 342 million people in the U.S., we can’t outsource its design. Rights-based AI is not a slogan. It’s a standard. It asks us to build systems that can be tested, trusted, and adapted by the very people they serve.
So yes, this is a moment for optimism. But it’s also a moment for accountability. For evaluation. For real-world testing. For listening to community context as much as technical specs. We don’t just need faster models. We need smarter foundations. Let’s build them together.
We the People.
About the Artwork:
You’ve seen glimpses of my artwork throughout this piece, visual meditations on what it means for AI to reflect not just data, but culture, memory, and meaning. As today’s headlines focus on global AI summits, open model debates, and the urgent push for governance, we can’t lose sight of the deeper question: Who gets to shape this future? My recent paintings — on display at Gallery 57 West in Annapolis and showing this fall at Pars Place in Virginia — offer one response.
Created through stenciling and silkscreen techniques, these works echo the way AI itself learns: through layers, through repetition, through pattern. With circuitry, ancient motifs, and binary code, they imagine AI not just as a tool of progress, but as a guardian of human complexity. And that’s a good thing — because we learn from history, we teach history, and we carry it forward.
Just as the America’s AI Action Plan calls for systems that are transparent, testable, and grounded in lived experience, this art reminds us that AI should learn from the world so it can truly reflect it.
My co-authored book “AI for Community,” now available from Taylor & Francis, explores how artificial intelligence can preserve cultural heritage, support human flourishing, and foster trustworthy, community-centered innovation.
Editorial note: I used AI to help shape and refine this blog, collaborating with a language model to enhance flow, clarity, and tone.
