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arxiv: 2606.19816 · v1 · pith:RSLKTSGXnew · submitted 2026-06-18 · 💻 cs.CY

Challenges to Grassroots Organization Engagement with AI Policy

Carter Buckner , Jennifer Mickel , Nandhini Swaminathan , William Agnew , Jacob Hobbs , Sarthak Arora , Michelle Lin , Yanan Long
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B.V. Alaka
This is my paper

Pith reviewed 2026-06-26 15:42 UTC · model grok-4.3

classification 💻 cs.CY
keywords participatory designAI policymarginalized communitiesgrassroots organizationsqueer communitiespolicy engagementpublic participationUS AI governance
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The pith

Grassroots organizations face distinct barriers when using participatory design to shape AI policy for marginalized communities.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper presents a case study of one organization's attempts to apply participatory design principles when developing AI policy for queer people through engagements with US policy bodies. It describes the practical difficulties that arise in these efforts and proposes suggestions to address them. A sympathetic reader would care because public participation serves as a key mechanism for accountability and alignment in AI governance, yet groups without extensive networks or lobbying power are often excluded. The authors focus on how these barriers manifest specifically with marginalized communities. Their work provides actionable recommendations for policymakers and organizers to improve inclusion.

Core claim

Through their engagements with several US policy bodies and participatory development of AI policy for queer people, the authors identify challenges with applying participatory design practice to marginalized communities and offer suggestions to alleviate those challenges.

What carries the argument

Participatory design (PD) principles applied to AI policymaking, which the case study shows are hindered by power imbalances and resource gaps in marginalized communities.

If this is right

  • Policymakers can adopt the suggestions to design more accessible processes for grassroots input on AI issues.
  • Other organizers working with marginalized communities can apply the recommendations to strengthen their policy engagement.
  • AI policies developed with these adjustments may better reflect the priorities of underrepresented groups.
  • Addressing the identified barriers could increase overall public accountability in AI governance.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same power and resource barriers may appear in grassroots efforts on other technology policy domains.
  • Building sustained networks and capacity for marginalized communities could amplify their influence across multiple policy areas.
  • Testing the suggested alleviations in different national or community contexts would clarify their general utility.

Load-bearing premise

The experiences and challenges from the authors' specific US policy engagements and work with queer communities represent broader difficulties for other grassroots organizations and marginalized groups.

What would settle it

Additional case studies or surveys in which multiple grassroots organizations successfully apply participatory design to AI policy without encountering the described challenges would undermine the claim of representativeness.

Figures

Figures reproduced from arXiv: 2606.19816 by B.V. Alaka, Carter Buckner, Jacob Hobbs, Jennifer Mickel, Michelle Lin, Nandhini Swaminathan, Sarthak Arora, William Agnew, Yanan Long.

Figure 1
Figure 1. Figure 1: A timeline of Queer in AI’s participation in US AI governance efforts. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Diagram illustrating power flow between “Users” (policy groups and the general public) and “Designers” (government [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An illustrative diagram of the design process for our organization’s policy explainer. Two full collaborative iterations [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

Public policies are being developed around the world to address privacy, economic, intellectual property, energy, and other risks that AI technologies pose. Involvement from the general public is essential to governance as an accountability and alignment mechanism. However, participating in and impacting policymaking can be challenging for sections of the public that lack extensive networks, lobbying capabilities, and other forms of power. This challenge is especially acute for marginalized communities. In this paper, we present a case study of our organization's efforts to bring participatory design (PD) principles to AI policymaking in the US. We describe our engagements with several US policy bodies, and our participatory development of AI policy for queer people. We highlight challenges with PD practice with marginalized communities, and offer suggestions to alleviate them. We conclude with actionable recommendations for policymakers and other organizers working in marginalized communities.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper presents a case study of the authors' grassroots organization applying participatory design (PD) principles to AI policymaking engagements in the US, with a focus on developing AI policy for queer people. It describes interactions with several US policy bodies, identifies challenges in PD practice with marginalized communities drawn from these experiences, offers suggestions to address them, and concludes with actionable recommendations for policymakers and organizers.

Significance. If the reported experiences hold, the work supplies concrete, practitioner-derived insights into barriers faced by grassroots groups in AI governance. This is valuable for the field of technology policy and public participation, as it surfaces practical suggestions that could inform more inclusive processes without requiring statistical claims.

minor comments (2)
  1. [Abstract] Abstract: the description of engagements with 'several US policy bodies' and the 'participatory development of AI policy for queer people' lacks any specifics on the bodies involved, timelines, or outcomes, which would help ground the case study for readers.
  2. The manuscript would benefit from a dedicated methods subsection clarifying how the PD sessions were structured, who participated, and how challenges were documented, to strengthen the transparency of the self-reported experiences.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their accurate summary of the manuscript and for recommending minor revision. Their assessment of the work's value in surfacing concrete, practitioner-derived insights into barriers for grassroots groups in AI governance aligns with our goals. No specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a qualitative case study reporting the authors' direct experiences with US policy engagements and participatory AI policy development for queer communities. Its claims consist of observed challenges and derived suggestions, with no equations, fitted parameters, mathematical derivations, or load-bearing self-citations that reduce any result to its own inputs by construction. The text explicitly frames the work as one organization's specific activities rather than asserting statistical representativeness, rendering the account self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a qualitative case study with no mathematical content. No free parameters, axioms, or invented entities are present.

pith-pipeline@v0.9.1-grok · 5693 in / 985 out tokens · 18001 ms · 2026-06-26T15:42:47.294438+00:00 · methodology

discussion (0)

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Reference graph

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