Whose AI?

Understanding how AI enters — and misses — everyday life. Who encounters it. Who shapes it. And who gets left out of the conversation entirely.

About

Why this matters

Lived experiences are central to understanding how AI will shape people’s lives

This is a moment when people can shape what gets built. Today’s AI discourse often focuses on capabilities, risks, and adoption. Yet far less attention is paid to how these systems are encountered in context. AI systems are still taking shape, but many assumptions guiding their development are already solidifying. Once embedded in products, institutions, and policy, assumptions become much harder to revisit.

We aim to understand how artificial intelligence is experienced by surfacing forms of experience, constraint, and judgment that are difficult to capture through existing methods. This work aims to both complement and challenge prevailing evidence bases to help researchers, policymakers, and technologists ground AI development and policy decisions in a broader range of real-world conditions.

Questions

Questions guiding this work

"Whose AI?" is our starting question

We want to hear from people wherever they are with AI. This work is about understanding how AI is experienced, interpreted, negotiated, or set aside in everyday life, and where those realities differ from the assumptions shaping current debates. It asks who engages AI directly and who is exposed through institutions, who finds it relevant and who does not, and who has meaningful opportunities to shape how these technologies evolve.

How is AI entering people's lives? And how much say do they have in it?

When people do encounter AI, what does it feel like: useful, intrusive, irrelevant, something harder to name?

Where AI falls short or doesn't fit, what would actually help? Better tools, broader access, stronger protections, clearer rules, or something else entirely?

Where do people see genuine possibility in what AI could do for them or their communities, or ways it could work better? And what do current conversations about AI seem to be missing?

Approach

Listening-first approach

We are not starting with fixed hypotheses

Context matters. We will learn from people's experiences in context. What feels urgent in one setting can be invisible in another. What barely registers as a problem for some can define the limits of what's possible for others.

We are especially focused on communities often missing from AI conversations — rural areas, families balancing work and caregiving, and places where the benefits of technological change have been uneven or slow to arrive. We hope to partner with individuals and communities to broaden whose experiences inform the next phase of technological change.