Three Lessons from Citizen-Centric Participatory AI Design
- URL: http://arxiv.org/abs/2602.08554v1
- Date: Mon, 09 Feb 2026 11:53:39 GMT
- Title: Three Lessons from Citizen-Centric Participatory AI Design
- Authors: Eike Schneiders, Sarah Kiden, Beining Zhang, Bruno Rafael Queiros Arcanjo, Zhaoxing Li, Ezhilarasi Periyathambi, Vahid Yazdanpanah, Sebastian Stein,
- Abstract summary: Drawing on three participatory workshops conducted in 2025 with members of the general public and cross-sector stakeholders, we explore how societal values and expectations shape visions of future AI agents.<n>We identify three key challenges: enabling meaningful and sustained public engagement, establishing a shared language between experts and lay participants, and translating speculative participant input into implementable systems.
- Score: 6.443236613659308
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This workshop paper examines challenges in designing agentic AI systems from a citizen-centric perspective. Drawing on three participatory workshops conducted in 2025 with members of the general public and cross-sector stakeholders, we explore how societal values and expectations shape visions of future AI agents. Using constructive design research methods, participants engaged in storytelling and lo-fi prototyping to reflect on potential community impacts. We identify three key challenges: enabling meaningful and sustained public engagement, establishing a shared language between experts and lay participants, and translating speculative participant input into implementable systems. We argue that reflexive, long-term participation is essential for responsible and actionable citizen-centric AI development.
Related papers
- AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
arXiv Detail & Related papers (2025-11-27T16:56:04Z) - Secondary Stakeholders in AI: Fighting for, Brokering, and Navigating Agency [4.874780144224057]
We theorize that meaningful participation involves three participatory ideals: informedness, (2) consent, and (3) agency.<n>We introduce three stakeholder archetypes: the reluctant data contributor, the unsupported activist, and the well-intentioned practitioner.<n>We envision an AI future where secondary stakeholders are able to meaningfully participate with the AI systems they influence and are influenced by.
arXiv Detail & Related papers (2025-06-08T20:57:30Z) - Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
General purpose AI seems to have lowered the barriers for the public to use AI and harness its power.
We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
arXiv Detail & Related papers (2024-03-21T19:12:37Z) - The Participatory Turn in AI Design: Theoretical Foundations and the
Current State of Practice [64.29355073494125]
This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation.
We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
arXiv Detail & Related papers (2023-10-02T05:30:42Z) - Retrospectives on the Embodied AI Workshop [238.302290980995]
We focus on 13 challenges presented at the Embodied AI Workshop at CVPR.
These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language.
We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models.
arXiv Detail & Related papers (2022-10-13T09:00:52Z) - Power to the People? Opportunities and Challenges for Participatory AI [9.504176941117493]
Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining momentum.
This paper reviews participatory approaches as situated in historical contexts as well as participatory methods and practices within the AI and ML pipeline.
We argue that as participatory AI/ML becomes in vogue, a contextual and nuanced understanding of the term as well as consideration of who the primary beneficiaries of participatory activities ought to be constitute crucial factors to realizing the benefits and opportunities that participation brings.
arXiv Detail & Related papers (2022-09-15T19:20:13Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - Empowering Local Communities Using Artificial Intelligence [70.17085406202368]
It has become an important topic to explore the impact of AI on society from a people-centered perspective.
Previous works in citizen science have identified methods of using AI to engage the public in research.
This article discusses the challenges of applying AI in Community Citizen Science.
arXiv Detail & Related papers (2021-10-05T12:51:11Z) - Progressing Towards Responsible AI [2.191505742658975]
Observatory on Society and Artificial Intelligence (OSAI) grew out of the project AI4EU.
OSAI aims to stimulate reflection on a broad spectrum of issues of AI (ethical, legal, social, economic and cultural)
arXiv Detail & Related papers (2020-08-11T09:46:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.