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REVIEW 3 major objections 5 minor 89 references

Designing for Constructive Civic Communication: A Framework for Human-AI Collaboration in Community Engagement Processes

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read AI in community engagement should be designed to keep both public leaders and community organizations in high control while using high automation, or it will fail to produce constructive civic communication.

desk verdict A useful but untested design framework for civic AI; the dual-control split is interesting, but the paper should confront the power tension between leaders and communities. read the letter →

arxiv 2505.11684 v1 pith:5DQJ3GBM submitted 2025-05-16 cs.HC

Cassandra Overney This is my paper · ORCID
classification cs.HC
keywords communityengagementhuman-AIcollaborationciviccommunicationconstructivehuman-centeredAIpublicparticipationdesignframeworkdemocraticgovernance
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that the value of AI in community engagement depends not on automation alone but on design that keeps two distinct human stakeholders, public leaders and community organizations, in control of decisions while the AI handles labor-intensive work. It maps these two forms of control onto a two-dimensional framework whose target quadrant combines high automation with high control for both groups. The paper also identifies two communication pathways whose health determines whether engagement succeeds: dialogue between leaders and organizations, and dialogue among organizations facilitated by leaders. It synthesizes known benefits and risks of AI in civic contexts and proposes five design considerations, critical engagement, flexibility, transparent boundaries, feedback loops, and reflection, that would make the target quadrant realizable. A sympathetic reader would care because this gives civic technology designers a concrete, evaluable target: use AI's capacity while preventing either side from losing agency.

What carries the argument

The central object is a two-dimensional human-AI collaboration framework adapted to civic communication. In the original framing, human control and computer automation are independent axes, producing a grid whose 'reliable, safe, and trustworthy' quadrant is high automation combined with high human control; the paper's adaptation splits human control into two axes, public leader control and community organization control, so the design target becomes the quadrant with high automation, high public leader control, and high community organization control. This object carries the argument because every design consideration in the paper, critical engagement, flexibility, transparent boundaries, feedback loops, and reflection and deliberation, is presented as a way to stay inside that quadrant.

What would settle it

A field experiment comparing an AI-assisted engagement tool set to high automation with dual high control against the same tool with, say, high public-leader control but low community-organization control; if community members report no greater agency, trust, or legitimacy under the dual-control condition, or if public leaders find that condition unworkable, the central claim is contradicted.

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Extended reading notes

Core claim

Stated in the paper's own terms, the central claim is that achieving beneficial human-AI collaboration in community engagement depends on design approaches that maintain a high degree of control in decision-making for both public leaders and community organizations while leveraging automation capabilities. The paper grounds this in an adapted two-dimensional framework in which human control and computer automation are separate axes: automation can be high while human control remains high, and in civic settings human control must be split into public leader control and community organization control. The recommended target is the quadrant with high automation plus high control on both stakeholder axes. Within that quadrant, the paper expects community engagement to exhibit the hallmarks of constructive communication: understanding, trust, respect, legitimacy, and agency. The conclusion follows from the paper's synthesis of the civic-communication literature, the documented failure modes of engagement, and the identified risks of unconstrained AI, including hallucination, opacity, bias, and over- or under-reliance.

Load-bearing premise

The load-bearing premise is that public leader control and community organization control are separable and that maximizing both together with automation produces more constructive civic communication than any other arrangement.

Editorial extensions

If this is right

  • Civic AI tools should not be built with a single human-in-the-loop; they need distinct control surfaces for public leaders and community organizations.
  • Automation that saves time but erodes community agency is a design failure, because the target quadrant requires both stakeholder controls to stay high.
  • The five design considerations give implementers a checklist: build in critical engagement with AI output, flexible interaction, transparent role boundaries, feedback loops, and reflective deliberation.
  • Transparency about when and how AI is used is not optional, because opaque AI threatens the perceived legitimacy that constructive civic communication requires.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the framework is right, the right metric for civic AI is not adoption or efficiency but changes in the five relationship outcomes, which the paper does not itself operationalize; building validated scales for those outcomes is a natural next step.
  • The two-control assumption could be tested directly with a platform that exposes separate control interfaces to each stakeholder group, then varies which controls are active; the paper does not describe such an experiment.
  • The same dual-control logic may apply to other two-party civic processes, such as participatory budgeting or school-district consultation, though the paper focuses on municipal engagement.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper is a conceptual essay that proposes a framework for human-AI collaboration in community engagement processes. It defines two communication pathways—between public leaders and community organizations, and among community organizations facilitated by public leaders—and characterizes constructive communication through outcomes such as understanding, trust, respect, legitimacy, and agency. The paper surveys challenges in community engagement, discusses benefits and risks of AI, adapts Shneiderman's human-centered AI framework to two types of human control (public leader control and community organization control), and concludes that beneficial human-AI collaboration requires high automation alongside high control for both stakeholder groups. It then lists five design considerations: critical engagement, flexibility, transparent boundaries, feedback loops, and reflection. The paper does not present empirical data or formal derivations; its central claim is asserted rather than tested.

Significance. If the framework's central claim is valid, the paper would provide a useful synthesis that connects civic-communication research with human-AI interaction design. The paper draws on a broad literature, articulates concrete design considerations, and grounds the discussion in existing systems including the author's own Coalesce, SenseMate, and BoundarEase. The two-pathway framing and the explicit attention to dual stakeholder control are potentially valuable contributions to a conceptual understanding of civic AI. However, the central claim that high automation plus high control for both public leaders and community organizations reliably produces constructive communication is not empirically or formally supported. The paper is best read as a position piece that generates hypotheses rather than a validated framework; its usefulness depends on subsequent operationalization and testing.

major comments (3)
  1. [§3.3, with §2.1] The adaptation of Shneiderman's framework in §3.3 splits human control into public leader control and community organization control, but the relationship between these two dimensions is not established. The paper justifies the split by analogy to elevators, which involve a single operator, not two stakeholder groups with potentially opposed interests. This assumption of independence is in tension with the paper's own citation of Arnstein in §2.1, where community power is described as requiring a redistribution of power from public leaders, implying a zero-sum relationship. If public leader control and community organization control are negatively correlated in real engagement settings, the high-high quadrant may be unattainable or ill-defined, and the central design directive loses its foundation. The authors should either provide evidence or argument for independence, or explicitly address the trade-off and define the conditions under which high-high is achievable.
  2. [§4 and §3.4] The central claim in §4—that achieving beneficial human-AI collaboration "depends on design approaches that maintain a high degree of control in decision-making for both public leaders and community organizations while leveraging automation capabilities"—is asserted rather than derived or measured. The design considerations in §3.4 are presented as implementing the framework, but no operational definitions are given for "control" or for the outcome constructs (understanding, trust, respect, legitimacy, agency), and no evidence is provided that these considerations are sufficient to produce constructive communication. The paper should reframe this as a hypothesis or a provisional framework, or it should provide case studies, empirical evaluations, or a formal argument connecting the design considerations to the claimed outcomes.
  3. [§3.3, Fig. 2] The two-dimensional grid in Fig. 2 appears to treat public leader control and community organization control as components of a single "Human Control" axis, but the text in §3.3 says these are "two distinct types of human control." The figure and text should clarify whether the two controls are orthogonal to each other, to automation, or both. Without this clarification, the reader cannot assess whether the high-high recommendation refers to a single point in a three-dimensional space or to a region in a two-dimensional space.
minor comments (5)
  1. [Fig. 2] The x-axis label "HighLow Computer Automation" appears garbled; the intended label is likely "Computer Automation" with a High/Low scale. The figure caption should also state that "Community Control" and "Public Leader Control" are introduced as refinements of Shneiderman's original "Human Control" dimension.
  2. [§3.2] There is a subject-verb agreement error in the sentence beginning "Hallucinations, which occur when AI generates false information..., poses significant risks"; "Hallucinations" is plural and should take "pose."
  3. [§1.2, Fig. 1] The legend in Fig. 1 includes a gray line for communication between community organizations without public leader involvement, but this pathway is not discussed in the paper's main analysis. The authors should either explain why it is excluded or remove it from the diagram to avoid confusion.
  4. [§3.4] The term "intentional stance" is introduced with a citation to Dennett but is not defined for a readership that may not be familiar with the philosophy of mind. A brief definition would improve accessibility.
  5. [References] Reference [45] (Klein) lacks full publication details; please provide the venue or repository information.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an explicitly proposed design framework anchored in external literature, not a derivation from fitted data or self-citation.

full rationale

The paper does not present a derivation chain in the sense of equations, fitted parameters, or empirical predictions. Its central claim, stated in §3.3 and restated in §4, is a normative design recommendation: that beneficial human-AI collaboration in community engagement requires high automation together with high control for both public leaders and community organizations. This recommendation is explicitly adapted from Shneiderman's external human-centered AI framework, and the paper quotes Shneiderman's general argument that high human control combined with high computer automation yields 'reliable, safe, and trustworthy systems.' The paper's contribution is the application and refinement of that framework to the civic context, not a prediction derived from data. The author's own prior systems (Coalesce, SenseMate, BoundarEase) are cited only as illustrative examples of design interventions that address specific challenges, such as question formulation and qualitative sensemaking; they are not used to justify the framework's central claim, to fit parameters, or to import an unverified uniqueness result. No self-citation is load-bearing in the argument. The skeptical concern that public-leader control and community-organization control may not be independent, or that the high-high quadrant may be internally tension-filled, is a substantive validity worry about the framework's assumptions, but it is not a circularity: the paper does not derive the independence assumption from its own conclusion. The conclusion's restatement of §3.3 is a summary of the proposed framework rather than a derivation of that framework from itself. Overall, the paper is self-contained as a design argument and exhibits no circular reasoning.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The framework rests on several domain assumptions about the value of community engagement, the definition of constructive communication, the transferability of Shneiderman's model, and the optimality of high-high control. No free parameters or invented entities are introduced because the paper is conceptual.

assumptions (5)
  • domain assumption Community engagement is a critical foundation of democratic governance and supports beneficial outcomes.
    Invoked throughout §1.1-1.2 as the premise for the paper's relevance; not empirically established in this paper.
  • domain assumption Constructive civic communication is defined by the outcome measures in §2: understanding, trust, respect, legitimacy, and agency for the leader-community pathway, and understanding, trust, respect, and willingness to engage for the community-community pathway.
    The paper treats these outcome measures as the target of design without deriving them from a broader theory; see §2.1 and §2.2.
  • domain assumption Shneiderman's two-dimensional framework, human control versus computer automation, is applicable to civic communication contexts.
    Stated in §3.3: 'we adapt Shneiderman's human-centered AI framework as particularly applicable to civic communication contexts.' This transfer is assumed.
  • domain assumption High human control and high computer automation can coexist and are optimal for civic processes.
    §3.3, based on the elevators example; this is a design value claim rather than an empirical fact.
  • ad hoc to paper The design considerations in §3.4, including critical engagement, flexibility, transparent boundaries, feedback loops, and reflection, are sufficient to achieve high dual control and constructive communication.
    These are the paper's proposed guidelines; their efficacy is not validated and they are presented as the framework's content.

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Cite this review

Pith. "Pith review of Designing for Constructive Civic Communication: A Framework for Human-AI Collaboration in Community Engagement Processes." pith.science (2026). https://pith.science/paper/5DQJ3GBM

@misc{pith2026250511684,
  author       = {Pith},
  title        = {Pith review of: Designing for Constructive Civic Communication: A Framework for Human-AI Collaboration in Community Engagement Processes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5DQJ3GBM}},
  note         = {Machine review of arXiv:2505.11684}
}
read the original abstract

Community engagement processes form a critical foundation of democratic governance, yet frequently struggle with resource constraints, sensemaking challenges, and barriers to inclusive participation. These processes rely on constructive communication between public leaders and community organizations characterized by understanding, trust, respect, legitimacy, and agency. As artificial intelligence (AI) technologies become increasingly integrated into civic contexts, they offer promising capabilities to streamline resource-intensive workflows, reveal new insights in community feedback, translate complex information into accessible formats, and facilitate reflection across social divides. However, these same systems risk undermining democratic processes through accuracy issues, transparency gaps, bias amplification, and threats to human agency. In this paper, we examine how human-AI collaboration might address these risks and transform civic communication dynamics by identifying key communication pathways and proposing design considerations that maintain a high level of control over decision-making for both public leaders and communities while leveraging computer automation. By thoughtfully integrating AI to amplify human connection and understanding while safeguarding agency, community engagement processes can utilize AI to promote more constructive communication in democratic governance.

Figures

Figures reproduced from arXiv: 2505.11684 by the authors.

Figure 1
Figure 1. A diagram of the key communication pathways in a typical community engagement process. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Shneiderman’s human-AI collaboration framework [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

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Works this paper leans on

89 extracted references · 30 canonical work pages

  1. [1]

    Landemore

    Tanja Aitamurto and Helene E. Landemore. 2015. Five Design Principles for Crowdsourced Policymaking: Assessing the Case of Crowdsourced Off-Road Traffic Law in Finland. https://papers.ssrn.com/abstract=2716769

    2015

  2. [2]

    1977.A Pattern Language

    Christopher Alexander. 1977.A Pattern Language. Oxford University Press, New York. Preface pp. ix-xvii; Patterns 165, 127, 98, 61, 62, 57, 18, 14, 242, 129

    1977

  3. [3]

    Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz

    Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz. 2019. Guidelines for Human-AI Interaction. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–13. doi:10....

    arXiv 2019

  4. [4]

    Arnstein

    Sherry R. Arnstein. 1969. A Ladder Of Citizen Participation.Journal of the American Institute of Planners35, 4 (July 1969), 216–224. doi:10.1080/ 01944366908977225

    1969

  5. [5]

    Eric P. S. Baumer, Mahmood Jasim, Ali Sarvghad, and Narges Mahyar. 2022. Of Course It’s Political! A Critical Inquiry into Underemphasized Dimensions in Civic Text Visualization.Computer Graphics Forum41, 3 (2022), 1–14. doi:10.1111/cgf.14518

    doi:10.1111/cgf.14518 2022

  6. [6]

    Doug Beeferman and Nabeel Gillani. 2023. FeedbackMap: A Tool for Making Sense of Open-ended Survey Responses. InCSCW ’23 Companion: Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing. 395–397. doi:10.1145/3584931.3607496

    arXiv 2023

  7. [7]

    Reuben Binns. 2018. Fairness in Machine Learning: Lessons from Political Philosophy. InProceedings of the 1st Conference on Fairness, Accountability and Transparency. PMLR, 149–159

    2018

  8. [8]

    Laura W. Black. 2008. Deliberation, Storytelling, and Dialogic Moments.Communication Theory18, 1 (Feb. 2008), 93–116. doi:10.1111/j.1468- 2885.2007.00315.x

    arXiv 2008

Show all 89 references
  1. [9]

    Herbert Blumer. 1948. Public Opinion and Public Opinion Polling.American Sociological Review13, 5 (1948), 542

    1948

  2. [10]

    Weinberg

    David Bohm and Robert A. Weinberg. 2004. On Dialogue. InOn Dialogue. Routledge, Chapter 2

    2004

  3. [11]

    Simon Bowen, Alexander Wilson, Sunil Rodger, Tom Feltwell, and Tom Nappey. 2023. Metro Futures 2020: Enabling Participation at Varying Depths and Scales via Digital Technology.International Journal of Human–Computer Interaction39, 18 (Nov. 2023), 3663–3683. doi:10.1080/1044731...

    2023

  4. [12]

    Bryson, Kathryn S

    John M. Bryson, Kathryn S. Quick, Carissa Schively Slotterback, and Barbara C. Crosby. 2013. Designing Public Participation Processes.Public Administration Review73, 1 (2013), 23–34. doi:10.1111/j.1540-6210.2012.02678.x

    2013

  5. [13]

    Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z. Gajos. 2021. To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making.Proceedings of the ACM on Human-Computer Interaction5, CSCW1 (April 2021), 188:1–188:21. doi:10.1145/3449287

    2021 doi

  6. [14]

    Burton, Mari-Klara Stein, and Tina Blegind Jensen

    Jason W. Burton, Mari-Klara Stein, and Tina Blegind Jensen. 2023. Beyond Algorithm Aversion in Human-Machine Decision-Making. InJudgment in Predictive Analytics, Matthias Seifert (Ed.). Springer International Publishing, Cham, 3–26. doi:10.1007/978-3-031-30085-1_1

    2023 doi

  7. [15]

    Glassman

    Alice Cai, Ian Arawjo, and Elena L. Glassman. 2024. Antagonistic AI. arXiv:2402.07350 [cs]

    2024 arXiv

  8. [16]

    Carpenter and W

    Susan L. Carpenter and W. J. D. Kennedy. 2001.Public Controversies and Conflict Management(2nd ed. ed.). Jossey-Bass, San Francisco

    2001

  9. [17]

    Y. Choi, E. J. Kang, S. Choi, M. K. Lee, and J. Kim. 2024. Proxona: Leveraging LLM-Driven Personas to Enhance Creators’ Understanding of Their Audience.arXiv(2024). doi:10.48550/arXiv.2408.10937 arXiv:2408.10937

    2024 doi arXiv

  10. [18]

    Le Dantec

    Eric Corbett and Christopher A. Le Dantec. 2019. ‘Removing Barriers’ and ‘Creating Distance’: Exploring the Logics of Efficiency and Trust in Civic Technology.Media and Communication7, 3 (Aug. 2019), 104–113. doi:10.17645/mac.v7i3.2154

    2019 doi

  11. [19]

    Le Dantec

    Eric Corbett and Christopher A. Le Dantec. 2018. The Problem of Community Engagement: Disentangling the Practices of Municipal Government. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18). Association for Computing Machinery, New York, N...

    2018

  12. [20]

    Fora” to Put Public Thought In Front of Policy Makers.Conference on Democratic “Frontsliding

    Katherine Cramer and Deb Roy. 2024. Using “Fora” to Put Public Thought In Front of Policy Makers.Conference on Democratic “Frontsliding”(2024)

    2024

  13. [21]

    Valdemar Danry, Pat Pataranutaporn, Yaoli Mao, and Pattie Maes. 2023. Don’t Just Tell Me, Ask Me: AI Systems That Intelligently Frame Explanations as Questions Improve Human Logical Discernment Accuracy over Causal AI Explanations. InProceedings of the 2023 CHI Conference on H...

    2023

  14. [22]

    D. C. (Daniel C.) Dennett. 1987.The Intentional Stance. MIT Press

    1987

  15. [23]

    The Cavalry Ain’t Coming in to Save Us

    Jessa Dickinson, Mark Díaz, Christopher A. Le Dantec, and Sheena Erete. 2019. "The Cavalry Ain’t Coming in to Save Us": Supporting Capacities and Relationships through Civic Tech.Proceedings of the ACM on Human-Computer Interaction3, CSCW (Nov. 2019), 123:1–123:21. doi:10.1145/3359225

    2019 doi

  16. [24]

    Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch. 2023. Improving factuality and reasoning in language models through multiagent debate.arXiv preprint arXiv:2305.14325(2023)

    2023 arXiv

  17. [25]

    Siamak Faridani, Ephrat Bitton, Kimiko Ryokai, and Ken Goldberg. 2010. Opinion space: a scalable tool for browsing online comments. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1175–1184. doi:10.1145/1753326.1753502

    2010

  18. [26]

    Parkes, Ariel D

    Sara Fish, Paul Gölz, David C. Parkes, Ariel D. Procaccia, Gili Rusak, Itai Shapira, and Manuel Wüthrich. 2023. Generative Social Choice. arXiv:2309.01291 [cs]

    2023 arXiv

  19. [27]

    Joel Fredericks, Martin Tomitsch, Luke Hespanhol, and Ian McArthur. 2015. Digital Pop-Up: Investigating Bespoke Community Engagement in Public Spaces. InProceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction (OzCHI ’15). Assoc...

    2015

  20. [28]

    Archon Fung. 2006. Varieties of Participation in Complex Governance.Public Administration Review66, s1 (Dec. 2006), 66–75. doi:10.1111/j.1540- 6210.2006.00667.x

    2006

  21. [29]

    Gajos and Lena Mamykina

    Krzysztof Z. Gajos and Lena Mamykina. 2022. Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning. In27th International Conference on Intelligent User Interfaces. ACM, Helsinki Finland, 794–806. doi:10.1145/3490099.3511138

    2022

  22. [30]

    2008.Political Communication and Deliberation

    John Gastil. 2008.Political Communication and Deliberation. SAGE Publications, Inc., Thousand Oaks, CA. Chapters 2 and 8

    2008

  23. [31]

    Xiao Ge, Chunchen Xu, Daigo Misaki, Hazel Rose Markus, and Jeanne L Tsai. 2024. How Culture Shapes What People Want From AI. InProceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24). Association for Computing Machinery, New York, NY, USA, 1–15. doi:1...

    2024

  24. [32]

    Gordon, Michelle S

    Mitchell L. Gordon, Michelle S. Lam, Joon Sung Park, Kayur Patel, Jeff Hancock, Tatsunori Hashimoto, and Michael S. Bernstein. 2022. Jury Learning: Integrating Dissenting Voices into Machine Learning Models. InCHI Conference on Human Factors in Computing Systems. ACM, New Orle...

    2022

  25. [33]

    2014.Action path : a location-based tool for civic reflection and engagement

    Erhardt (Erhardt Charles) Graeff. 2014.Action path : a location-based tool for civic reflection and engagement. Thesis. Massachusetts Institute of Technology. https://dspace.mit.edu/handle/1721.1/95604 Accepted: 2015-02-25T17:13:21Z Journal Abbreviation: Location-based tool fo...

    2014

  26. [34]

    David Hagmann, Julia Minson, and Catherine Tinsley. 2020. Personal Narratives Build Trust Across Ideological Divides. doi:10.31219/osf.io/sw7nz

    2020 doi

  27. [35]

    Eric Horvitz. 1999. Principles of Mixed-Initiative User Interfaces. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’99). Association for Computing Machinery, New York, NY, USA, 159–166. doi:10.1145/302979.303030

    1999

  28. [36]

    Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. 2023. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Infor...

    2023

  29. [37]

    Irvin and John Stansbury

    Renée A. Irvin and John Stansbury. 2004. Citizen Participation in Decision Making: Is It Worth the Effort?Public Administration Review64, 1 (2004), 55–65. doi:10.1111/j.1540-6210.2004.00346.x

    2004

  30. [38]

    Mahmood Jasim, Enamul Hoque, Ali Sarvghad, and Narges Mahyar. 2021. CommunityPulse: Facilitating Community Input Analysis by Surfacing Hidden Insights, Reflections, and Priorities. InDesigning Interactive Systems Conference 2021. ACM, Virtual Event USA, 846–863. doi:10.1145/34...

    2021 doi

  31. [39]

    Zhang, Ali Sarvghad, and Narges Mahyar

    Mahmood Jasim, Pooya Khaloo, Somin Wadhwa, Amy X. Zhang, Ali Sarvghad, and Narges Mahyar. 2021. CommunityClick: Capturing and Reporting Community Feedback from Town Halls to Improve Inclusivity.Proceedings of the ACM on Human-Computer Interaction4, CSCW3 (Jan. 2021), 213:1–213...

    2021 doi

  32. [40]

    Zhiqiu Jiang, Mashrur Rashik, Kunjal Panchal, Mahmood Jasim, Ali Sarvghad, Pari Riahi, Erica DeWitt, Fey Thurber, and Narges Mahyar. 2023. CommunityBots: Creating and Evaluating A Multi-Agent Chatbot Platform for Public Input Elicitation.Proceedings of the ACM on Human-Compute...

    2023 doi

  33. [41]

    Kalla and David E

    Joshua L. Kalla and David E. Broockman. 2023. Which Narrative Strategies Durably Reduce Prejudice? Evidence from Field and Survey Experiments Supporting the Efficacy of Perspective-Getting.American Journal of Political Science67, 1 (2023), 185–204. doi:10.1111/ajps.12657

    2023 doi

  34. [42]

    Perrault, Jihee Kim, and Juho Kim

    Hyunwoo Kim, Eun-Young Ko, Donghoon Han, Sung-Chul Lee, Simon T. Perrault, Jihee Kim, and Juho Kim. 2019. Crowdsourcing Perspectives on Public Policy from Stakeholders. InExtended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA ’19). Associat...

    2019

  35. [43]

    Juho Kim, Eun-Young Ko, Jonghyuk Jung, Chang Won Lee, Nam Wook Kim, and Jihee Kim. 2015. Factful: Engaging Taxpayers in the Public Discussion of a Government Budget. InProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15). Association fo...

    2015

  36. [44]

    Help Me Help the AI

    Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, Ruth Fong, and Andrés Monroy-Hernández. 2023. "Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems (C...

    2023

  37. [45]

    Mark Klein. 2007. How to Harvest Collective Wisdom for Complex Problems: An Introduction to the MIT Deliberatorium. doi:10.13140/RG.2.2. 32743.24489

    2007

  38. [46]

    Everyone Is Talking about It!

    Lisa Koeman, Vaiva Kalnikaité, and Yvonne Rogers. 2015. "Everyone Is Talking about It!": A Distributed Approach to Urban Voting Technology and Visualisations. InProceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15). Association for Compu...

    2015

  39. [47]

    Katerina Korre, Dimitris Tsirmpas, Nikos Gkoumas, Emma Cabalé, Dionysis Kontarinis, Danai Myrtzani, Theodoros Evgeniou, Ion Androutsopoulos, and John Pavlopoulos. 2025. Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey. arXiv:2503.01513 [cs.CL] https: ...

    2025

  40. [49]

    Travis Kriplean, Michael Toomim, Jonathan Morgan, Alan Borning, and Amy J. Ko. 2012. Is this what you meant? promoting listening on the web with reflect. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’12). Association for Computing Machinery...

    2012

  41. [50]

    Emily Kubin, Curtis Puryear, Chelsea Schein, and Kurt Gray. 2021. Personal Experiences Bridge Moral and Political Divides Better than Facts. Proceedings of the National Academy of Sciences118, 6 (Feb. 2021), e2008389118. doi:10.1073/pnas.2008389118

    2021 doi

  42. [51]

    Vivian Lai and Chenhao Tan. 2019. On human predictions with explanations and predictions of machine learning models: A case study on deception detection. InProceedings of the conference on fairness, accountability, and transparency. 29–38

    2019

  43. [52]

    Le Dantec, Mariam Asad, Anirudh Misra, and Kari E

    Christopher A. Le Dantec, Mariam Asad, Anirudh Misra, and Kari E. Watkins. 2015. Planning with Crowdsourced Data: Rhetoric and Representation in Transportation Planning. InProceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. 1717–17...

    2015

  44. [53]

    Procaccia

    Min Kyung Lee, Daniel Kusbit, Anson Kahng, Ji Tae Kim, Xinran Yuan, Allissa Chan, Daniel See, Ritesh Noothigattu, Siheon Lee, Alexandros Psomas, and Ariel D. Procaccia. 2019. WeBuildAI: Participatory Framework for Algorithmic Governance.Proceedings of the ACM on Human-Computer...

    2019 doi

  45. [54]

    Haotian Li, Yun Wang, and Huamin Qu. 2024. Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration. InProceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24). Association for Computing Machinery, New Y...

    2024

  46. [55]

    James, Michelle M

    Narges Mahyar, Michael R. James, Michelle M. Ng, Reginald A. Wu, and Steven P. Dow. 2018. CommunityCrit: Inviting the Public to Improve and Evaluate Urban Design Ideas through Micro-Activities. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI...

    2018

  47. [56]

    Nguyen, Maggie Chan, Jiayi Zheng, and Steven P

    Narges Mahyar, Diana V. Nguyen, Maggie Chan, Jiayi Zheng, and Steven P. Dow. 2019. The Civic Data Deluge: Understanding the Challenges of Analyzing Large-Scale Community Input. InProceedings of the 2019 on Designing Interactive Systems Conference. ACM, San Diego CA USA, 1171–1...

    2019

  48. [57]

    Diana C. Mutz. 2006. Benefits of Hearing the Other Side. InHearing the Other Side: Deliberative versus Participatory Democracy. Cambridge University Press, Cambridge, Chapter 3

    2006

  49. [58]

    Matti Nelimarkka. 2019. A Review of Research on Participation in Democratic Decision-Making Presented at SIGCHI Conferences. Toward an Improved Trading Zone Between Political Science and HCI.Proceedings of the ACM on Human-Computer Interaction3, CSCW (Nov. 2019), 139:1–139:29....

    2019 doi

  50. [59]

    Matti Nelimarkka, Brandie Nonnecke, Sanjay Krishnan, Tanja Aitumurto, Daniel Catterson, Camille Crittenden, Chris Garland, Conrad Gregory, Ching-Chang (Allen) Huang, Gavin Newsom, Jay Patel, John Scott, and Ken Goldberg. 2014. Comparing Three Online Civic Engagement Platforms ...

    2014

  51. [60]

    Kessler, Suyash P

    Cassandra Overney, Daniel T. Kessler, Suyash P. Fulay, Mahmood Jasim, and Deb Roy. 2025. Coalesce: An Accessible Mixed-Initiative System for Designing Community-Centric Questionnaires. InProceedings of the 30th International Conference on Intelligent User Interfaces (IUI ’25)(...

    2025

  52. [61]

    Cassandra Overney, Cassandra Moe, Alvin Chang, and Nabeel Gillani. 2025. BoundarEase: Fostering Constructive Community Engagement to Inform More Equitable Student Assignment Policies.Proceedings of the ACM on Human-Computer Interaction9, 2 (April 2025), 37. doi:10.1145/3710938

    2025 doi

  53. [62]

    Cassandra Overney, Belén Saldías, Dimitra Dimitrakopoulou, and Deb Roy. 2024. SenseMate: An Accessible and Beginner-Friendly Human-AI Platform for Qualitative Data Analysis. InProceedings of the 29th International Conference on Intelligent User Interfaces (IUI ’24). Associatio...

    2024

  54. [63]

    Parker J. Palmer. 2009.A Hidden Wholeness: The Journey Toward an Undivided Life. Jossey-Bass, San Francisco. Chapter 3 (pp. 32-49) and Chapter 6 (pp. 90-111)

    2009

  55. [64]

    Zhong-Ren Peng, Kai-Fa Lu, Yanghe Liu, and Wei Zhai. 2023. The Pathway of Urban Planning AI: From Planning Support to Plan-Making.Journal of Planning Education and Research(June 2023), 0739456X231180568. doi:10.1177/0739456X231180568

    2023 doi

  56. [65]

    Robert D. Putnam. 1995. Bowling Alone: America’s Declining Social Capital.Journal of Democracy6, 1 (1995), 65–78. doi:10.1353/jod.1995.0002

    1995

  57. [66]

    Taylor Regan, David Sweeney, John Helmes, Vasilis Vlachokyriakos, Siân Lindley, and Alex Taylor. 2015. Designing Engaging Data in Communities. InProceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. 271–274. doi:10.1145/2702613.2725432

    2015

  58. [67]

    Dow, and Narges Mahyar

    Brandon Reynante, Steven P. Dow, and Narges Mahyar. 2021. A Framework for Open Civic Design: Integrating Public Participation, Crowdsourcing, and Design Thinking.Digital Government: Research and Practice2, 4 (Oct. 2021), 1–22. doi:10.1145/3487607

    2021 doi

  59. [68]

    Nancy C. Roberts. 2015.The Age of Direct Citizen Participation. Routledge, Abington

    2015

  60. [69]

    Deb Roy. 2021. Trust, Society and Democracy: The Role of Social Media. https://www.youtube.com/watch?v=hARzzuoklGI

    2021

  61. [70]

    E. E. Schattschneider. 1960. The Contagiousness of Conflict. InThe Semisovereign People: A Realist’s View of Democracy in America. Holt, Rinehart and Winston, New York, 1–19

    1960

  62. [71]

    E. E. Schattschneider. 1969.Two Hundred Million Americans in Search of a Government(first edition ed.). Holt, Rinehart and Winston. Chapter 1 to 4 (pp. 3-78)

    1969

  63. [72]

    Juliana Schroeder, Michael Kardas, and Nicholas Epley. 2017. The Humanizing Voice: Speech Reveals, and Text Conceals, a More Thoughtful Mind in the Midst of Disagreement.Psychological Science28, 12 (December 2017), 1745–1762. doi:10.1177/0956797617713798

    2017 doi

  64. [73]

    Ronald Schroeter. 2012. Engaging new digital locals with interactive urban screens to collaboratively improve the city. InProceedings of the ACM 2012 conference on Computer Supported Cooperative Work (CSCW ’12). Association for Computing Machinery, New York, NY, USA, 227–236. ...

    2012

  65. [74]

    E. F. Schumacher. 1973.Small is Beautiful: A Study of Economics As If People Mattered. Blond & Briggs. Part I: Sections 3-5; Part II: Section 5

    1973

  66. [75]

    Schumpeter

    Joseph A. Schumpeter. 2010.Capitalism, Socialism and Democracy. Taylor & Francis Group, Oxford, UNITED KINGDOM

    2010

  67. [76]

    Ben Shneiderman. 2020. Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy.International Journal of Human–Computer Interaction 36, 6 (April 2020), 495–504. doi:10.1080/10447318.2020.1741118

    2020

  68. [77]

    Slattery, A

    P. Slattery, A. K. Saeri, E. A. C. Grundy, J. Graham, M. Noetel, R. Uuk, J. Dao, S. Pour, S. Casper, and N. Thompson. 2024. A Systematic Evidence Review and Common Frame of Reference for the Risks from Artificial Intelligence. doi:10.48550/arXiv.2408.12622 arXiv:2408.12622

    2024 doi arXiv

  69. [78]

    Christopher Small. 2021. Polis: Scaling Deliberation by Mapping High Dimensional Opinion Spaces.RECERCA. Revista de Pensament i Anàlisi(July 2021). doi:10.6035/recerca.5516

    2021 doi

  70. [79]

    Small, Ivan Vendrov, Esin Durmus, Hadjar Homaei, Elizabeth Barry, Julien Cornebise, Ted Suzman, Deep Ganguli, and Colin Megill

    Christopher T. Small, Ivan Vendrov, Esin Durmus, Hadjar Homaei, Elizabeth Barry, Julien Cornebise, Ted Suzman, Deep Ganguli, and Colin Megill

  71. [80]

    2024.To Help Improve the Accuracy of Generative AI, Add Speed Bumps

    Beth Stackpole. 2024.To Help Improve the Accuracy of Generative AI, Add Speed Bumps. https://mitsloan.mit.edu/ideas-made-to-matter/to-help- improve-accuracy-generative-ai-add-speed-bumps

    2024

  72. [81]

    Stains Jr

    Robert R. Stains Jr. 2012. Reflection for Connection: Deepening Dialogue through Reflective Processes.Conflict Resolution Quarterly30, 1 (2012), 33–51. doi:10.1002/crq.21053

    2012 doi

  73. [82]

    Frohlich, Peter Wright, and Patrick Olivier

    Nick Taylor, Justin Marshall, Alicia Blum-Ross, John Mills, Jon Rogers, Paul Egglestone, David M. Frohlich, Peter Wright, and Patrick Olivier. 2012. Viewpoint: Empowering Communities with Situated Voting Devices. InProceedings of the SIGCHI Conference on Human Factors in Compu...

    2012

  74. [83]

    Li Tian, Jinxuan Liu, Yinlong Liang, and Yaxin Wu. 2023. A participatory e-planning model in the urban renewal of China: Implications of technologies in facilitating planning participation.Environment and Planning B: Urban Analytics and City Science50, 2 (Feb. 2023), 299–315. ...

    2023 doi

  75. [84]

    Nina Valkanova, Robert Walter, Andrew Vande Moere, and Jörg Müller. 2014. MyPosition: Sparking Civic Discourse by a Public Interactive Poll Visualization. InProceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’14). Association...

    2014

  76. [85]

    Nilay Yavuz. 2023. Digital Divide and Citizen Participation in Public Policy Making. InCitizen-Centered Public Policy Making in Turkey, Volkan Göçoğlu and Naci Karkin (Eds.). Springer International Publishing, Cham, 115–130. doi:10.1007/978-3-031-35364-2_7 14 Overney

    2023 doi

  77. [86]

    Angie Zhang, Olympia Walker, Kaci Nguyen, Jiajun Dai, Anqing Chen, and Min Kyung Lee. 2023. Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation.Proceedings of the ACM on Human-Computer Interaction7, CSCW1 ...

    2023 doi

  78. [87]

    He Zhang, Chuhao Wu, Jingyi Xie, Yao Lyu, Jie Cai, and John M. Carroll. 2023. Redefining Qualitative Analysis in the AI Era: Utilizing ChatGPT for Efficient Thematic Analysis. arXiv:2309.10771 [cs]

    2023 arXiv

  79. [88]

    Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al. 2023. Siren’s song in the AI ocean: a survey on hallucination in large language models.arXiv preprint arXiv:2309.01219(2023)

    2023 arXiv

  80. [89]

    Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy. 2020. Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. InProceedings of the 2020 conference on fairness, accountability, and transparency. 295–305

    2020

  81. [2023]

    doi:10.48550/arXiv.2306.11932 arXiv:2306.11932 [cs]

    Opportunities and Risks of LLMs for Scalable Deliberation with Polis. doi:10.48550/arXiv.2306.11932 arXiv:2306.11932 [cs]

    doi arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.