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TRACE Project

Technocultural Research on Algorithms & Community Experiences

Using ethnography to examine how different communities experience and perceive AI systems

Subtle social impacts are often overlooked in AI policy research

Ethnographic methods have been largely underutilized in AI policy research, resulting in policy frameworks that fail to align with the nuanced, lived experiences of diverse communities interacting with AI systems. Subtle but meaningful social impacts - both harms and benefits - frequently go unrecognized in policy discussions, while subversive use-cases that emerge organically within communities are often missed as well. Our concern is that this gap between how AI is discussed in policy and research circles versus how it is experienced on the ground by real users could lead to interventions that may not address the actual needs or concerns of the public.

The goal of TRACE is to fill these gaps by using ethnographic methods to supplement existing research on AI's societal impacts. We'll conduct netnographic studies of online communities; engage in ethnographic interviews with key populations; leverage deliberative tools to deepen the insights we uncover; and share our findings through research reports and creative outputs.

T.R.A.C.E explores how different communities are encountering AI

How are different communities actively using current AI tools, or being involuntarily impacted by AI systems?

How do different communities feel towards critical topics in AI, and how informed are they about AI risks?

How do experiences with AI differ between sectors, as well as across occupations within the same sector?

What subversive or creative use cases are emerging within different communities?

How have community responses to AI shifted over time, particularly following controversial incidents, political events, or technical breakthroughs?

We use netnography of online communities to capture the difficult-to-quantify social impacts of AI systems. By using ethnographic methods to uncover contextualized insights into people’s first-hand experiences and social practices, our research aims to provide a complementary perspective to existing empirical research within the AI policy space. Some of the questions we'll explore include:

Informing AI governance approaches that are responsive to communities

By capturing the experiential dimensions of AI's presence within different communities, TRACE will help surface regulatory blind spots that predominantly quantitative approaches often miss, aiding in the development of policies and risk-management frameworks that are better aligned with the first-hand experiences of real people. Examining on-the-ground use cases across distinct occupational contexts will also enable us to generate sector-specific insights that more accurately reflect the unique challenges faced by different professions, ensuring that policy recommendations are attuned to the realities of how AI is being used and felt by workers.

Beyond its implications for AI policy and labor research, TRACE will also contribute to existing efforts that seek to create a historical record of public sentiment towards AI. We currently lack systematic documentation of how different groups are experiencing AI's rapid evolution, so our hope is that archival work like TRACE will help preserve how communities were experiencing AI during this time of profound transformation.

TRACE is brand new initiative - check back in soon to see what we're up to!