Pith. sign in

REVIEW 2 major objections 5 minor 125 references

Regulating AI: Where U.S. State Policy and HCI (Mis)align

T0 review · 2 major / 5 minor · reviewed 2026-07-12 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read U.S. state AI committees emphasize benefits over risks and omit many socio-technical harms that HCI research prioritizes.

desk verdict Solid first map of what U.S. state AI committees actually write about benefits vs. risks, with transparent coding and a useful (if asymmetric) comparison to the AI Risk Repository. read the letter →

arxiv 2607.03292 v1 pith:CVPUEQWP submitted 2026-07-03 cs.HC

Nino Migineishvili , Alice Gao , Adinawa Adjagbodjou , Dhanaraj Thakur , René Just , Katharina Reinecke This is my paper · ORCID
classification cs.HC
keywords ArtificialIntelligenceAIPolicyGovernanceRegulationHCI-PolicyInteractionSocio-TechnicalApproachesBenefitsRisks
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

U.S. states have formed AI committees whose published reports reveal how policymakers weigh AI trade-offs amid thin federal guidance. This paper analyzes all 18 existing reports and finds that committees systematically discuss benefits more deeply than risks across economic sectors. When risks are raised, they only partially match the categories emphasized in HCI and related literature: discrimination and misinformation appear, but high-stakes socio-technical issues such as loss of human agency, AI pursuing conflicting goals, power centralization, and mass-harm scenarios receive little attention. Recommendations for literacy, risk assessment, inclusion, and human oversight are common yet often vague and definitionally under-specified. The authors argue that HCI methods—participatory design, clearer terminology, and socio-technical framing—can help close the gap while policy is still being written.

What carries the argument

A mixed-methods comparison of 18 state AI committee reports against a taxonomy of AI risks drawn from HCI and related literature, using systematic scoring of benefit/risk depth by sector and chi-squared / residual analysis of risk-category frequencies.

What would settle it

A re-coding of the same 18 reports against an independently constructed, non-risk-selected sample of HCI papers on AI, or interviews with committee members that show their risk priorities match the literature even when the written reports do not.

Watch

Extended reading notes

Core claim

State AI committee reports systematically emphasize benefits over risks (confirmed by Wilcoxon signed-rank test across sectors) and the risks they do discuss differ significantly in distribution from those catalogued in HCI literature, under-representing socio-technical categories such as AI pursuing goals that conflict with human values, loss of human agency, cyberattacks and mass harm, and power centralization.

Load-bearing premise

The assumption that how often a risk appears in a literature corpus built to catalogue risks is a fair baseline for what HCI prioritizes, so that the same frequency metric applied to committee reports measures genuine misalignment rather than a difference in genre or mandate.

Editorial extensions

If this is right

  • Policymakers who rely mainly on these reports will receive an incomplete map of AI harms, especially systemic and high-stakes ones.
  • HCI researchers can target the documented gaps—agency, power, environmental cost, definitional precision—while state rules are still being drafted.
  • Participatory methods and standardized AI definitions become concrete levers for making committee recommendations operational rather than aspirational.
  • States that already have committees are more likely to pass consumer-protection laws, so the content of these reports shapes early regulatory trajectories.

Reading between the lines

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

  • The same benefit-over-risk pattern and socio-technical omissions are likely to reappear in forthcoming federal or multi-state model legislation that draws on these reports.
  • Definitional vagueness around “AI,” “high-risk,” and “transparency” will make enforcement of any resulting statutes uneven across states.
  • Industry-heavy committee membership may systematically narrow the risk set that reaches the written record even when public-participation language is present.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The paper conducts a mixed-methods content analysis of all 18 published U.S. state AI committee reports. It develops a typology of motivations (economic growth, government operations, responsible governance), scores sector-specific benefit and risk discussions on a 0-1-2 depth scale, and shows via Wilcoxon signed-rank (p<0.0001) plus mixed-effects logistic robustness checks that benefits are systematically emphasized over risks. Risks are then mapped onto the AI Risk Repository taxonomy; a chi-squared test (χ²=40.04, p=0.015) and standardized residuals indicate under-representation of high-stakes socio-technical categories (e.g., AI goal conflict, loss of agency, mass harm, power centralization) relative to HCI-related literature. Thematic analysis of mitigation strategies reveals definitional ambiguity, limited stakeholder diversity, and generic recommendations. The authors conclude that committees invoke responsible AI yet omit broader socio-technical framings and outline HCI opportunities (participatory design, literacy tools, definitional standardization) to close the gap.

Significance. The work supplies a timely, systematic empirical baseline of how U.S. state policymakers currently frame AI trade-offs at a moment when federal guidance is absent and state committees are actively shaping legislation. Strengths include an explicit codebook (Appendix B), dual-coding calibration on a 39% sample, transparent 0-1-2 scoring rules, non-parametric and mixed-effects statistical checks (Appendix A), and an open acknowledgment of the Repository comparison asymmetry. If the coded patterns hold, the paper offers concrete mileposts for HCI researchers seeking proactive rather than reactive policy engagement and for policymakers seeking clearer socio-technical language.

major comments (2)
  1. §4.4.1 and §4.4.4: After dual coding a 39% sample to build the codebook, a single author applied all codes and performed the Repository risk mapping, with only post-hoc presentation for agreement. No inter-rater reliability statistic (e.g., Cohen’s κ or percent agreement on the full set) is reported. Because the central claims rest on the resulting frequency counts and residual rankings (Table 4), a reliability check on a second independent coding of at least the risk-mapping step is needed to confirm that the observed divergences are not coder-specific.
  2. §4.4.4 and Table 4: The authors correctly flag the genre asymmetry (risk-cataloguing literature vs. variable-mandate committee reports) yet still frame the χ² result and residual outliers as evidence of “misalignment” in the abstract, title, and §5.3. The benefit-over-risk finding is independent and robust; the Repository comparison is only a prevalence proxy. Softening the causal language of “misalign” to “differ in emphasis” (or adding a short sensitivity discussion of how genre differences alone could produce the residual pattern) would keep the claim proportionate to the evidence.
minor comments (5)
  1. Figure 1 caption and §4.4.3: Clarify whether the plotted scores are raw sums or averages across states; the current wording leaves the y-axis scale ambiguous.
  2. Table 4: The residual cutoff |1.28| (90% confidence) is stated but not justified relative to the more conventional |1.96|. A one-sentence rationale or a sensitivity column at 95% would help readers assess robustness.
  3. §5.4.2: Several long block quotations from reports are lightly edited for readability; indicate the nature of the edits (e.g., ellipses, grammar) more explicitly.
  4. Appendix B codebook: A few low-level codes (e.g., “Unintended Consequences Concerns”) appear under Motivation yet are not quantified in the main text; either drop them or report their frequencies for completeness.
  5. References: The AI Risk Repository citation is dated March 2025; confirm the exact snapshot version used so that future replications can match the taxonomy.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical coding of external reports vs. an independent published taxonomy; statistics describe coded frequencies rather than recover fitted inputs.

full rationale

The paper is a mixed-methods content analysis of 18 publicly available U.S. state AI committee reports. Benefits/risks are scored 0/1/2 by depth of discussion (Section 4.4.3), then compared within-report via Wilcoxon signed-rank and mixed-effects logistic robustness checks (Appendix A); risks are mapped one-to-one onto the external AI Risk Repository taxonomy (Section 4.4.4) and compared by chi-squared / standardized residuals. None of these steps define a quantity in terms of itself, fit a free parameter and re-label it a prediction, or rest on a uniqueness theorem or ansatz imported from the authors’ own prior work. The authors themselves flag the genre asymmetry between risk-cataloguing literature and variable-mandate committee reports; that caveat weakens interpretive rhetoric but does not make any reported statistic tautological. Self-citations (e.g., to HCI-policy workshops) are ordinary background and not load-bearing for the coded patterns or statistical tests. The derivation chain is therefore self-contained against external documents and contains no circular reduction.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

Empirical social-science paper; load-bearing elements are coding conventions, the choice of external taxonomy, and interpretive frames rather than free parameters or invented physical entities.

free parameters (2)
  • benefit/risk depth score (0/1/2)
    Ordinal scoring of discussion depth per sector; thresholds for 'brief' vs 'substantive' are researcher-defined (Section 4.4.3).
  • standardized residual cutoff |1.28|
    90 % confidence threshold used to flag over-/under-represented risk subdomains (Section 5.3).
assumptions (4)
  • domain assumption The AI Risk Repository (March 2025 version) is a valid proxy for the distribution of AI risks discussed in HCI and closely related fields.
    Explicitly adopted in Section 4.4.4 as the comparison baseline; the paper notes HCI is not its sole home discipline.
  • domain assumption Frequency of mention in committee reports is a usable proxy for policymaker prioritization.
    Underpins the quantitative comparison in RQ3; authors treat it as rough but informative.
  • domain assumption Committee reports (rather than enacted statutes or private deliberations) reveal the deliberative priorities of state AI policymakers.
    Stated scope choice in Sections 2.2 and 4.2; limitations section acknowledges the proxy nature.
  • domain assumption Social Construction of Technology (SCOT) and ex-ante risk regulation frames are appropriate lenses for interpreting the reports.
    Used in Discussion (Sections 6.1–6.2) to situate findings.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Regulating AI: Where U.S. State Policy and HCI (Mis)align." pith.science (2026). https://pith.science/paper/CVPUEQWP

@misc{pith2026260703292,
  author       = {Pith},
  title        = {Pith review of: Regulating AI: Where U.S. State Policy and HCI (Mis)align},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CVPUEQWP}},
  note         = {Machine review of arXiv:2607.03292}
}
read the original abstract

Artificial intelligence (AI) technologies are increasingly adopted into everyday life, with most investment and development concentrated in the U.S. In response to rapid AI integration and scant federal guidelines, U.S. states have formed AI committees charged with studying AI-related societal trade-offs. We analyzed the 18 existing state-level AI committee reports to understand how policymakers discuss AI-related benefits and risks. We then compared the risks surfaced by policymakers to an established taxonomy of AI risks aggregated from literature and examined how policymakers' concerns align, or misalign, from those of HCI scholars. These insights provide important mileposts for shaping currently ongoing policy initiatives and future research. Our findings reveal important gaps: while committees invoke responsible AI, their framings often omit broader socio-technical concerns emphasized in HCI. We discuss opportunities for HCI to support socio-technical perspectives, employ participatory design, and close the gap between research and policy.

Figures

Figures reproduced from arXiv: 2607.03292 by the authors.

Figure 1
Figure 1. AI-Related Benefits Versus Risks Across Sectors. We assessed sectors and domains states described as benefiting from [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. AI Committees and Reports Across U.S. States: 31 states have established AI committees (highlighted in pink, hatched), [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Motivation Typology: State-level policymakers were motivated to form AI committees to pursue responsible gover [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

125 extracted references · 2 canonical work pages

  1. [1]

    2025.Impact of Artificial Intelligence on the Global Economy and Technology Advancements

    Muhammad Abbas Khan, Habib Khan, Muhammad Faizan Omer, Inam Ullah, and Muhammad Yasir. 2025.Impact of Artificial Intelligence on the Global Economy and Technology Advancements. Springer Nature Singapore, Singapore, 147–180. doi:10.1007/978-981-97-3222-7_7 2https://wildlab.cs.washington.edu/

    work page doi:10.1007/978-981-97-3222-7_7 2025

  2. [2]

    Mark S Ackerman. 2000. The intellectual challenge of CSCW: the gap between social requirements and technical feasibility.Human–Computer Interaction15, 2-3 (2000), 179–203

    2000

  3. [3]

    EU Artificial Intelligence Act. 2024. The eu artificial intelligence act.European Union(2024)

    2024

  4. [4]

    Blair Attard-Frost, Ana Brandusescu, and Kelly Lyons. 2024. The governance of artificial intelligence in Canada: Findings and opportunities from a review of 84 AI governance initiatives.Government Information Quarterly41, 2 (2024), 101929

    2024

  5. [5]

    Sumitran Basu. 2023. Three decades of social construction of technology: dy- namic yet fuzzy? The methodological conundrum.Social Epistemology37, 3 (2023), 259–275

    2023

  6. [6]

    Trust Us

    Ghassan F Bati and Vivek K Singh. 2018. “Trust Us” Mobile Phone Use Patterns Can Predict Individual Trust Propensity. InProceedings of the 2018 CHI conference on human factors in computing systems. 1–14

    2018

  7. [7]

    Amna Batool, Didar Zowghi, and Muneera Bano. 2025. AI governance: a sys- tematic literature review.AI and Ethics(2025), 1–15

    2025

  8. [8]

    Brando Benifei and Ioan-Dragos Tudorache. 2023. Proposal for a Regulation of the European Parliament and of the Council on Harmonised Rules on Arti- ficial Intelligence (Artificial Intelligence Act).Draft compromise amendments, European Parliament16 (2023)

    2023

Show all 125 references
  1. [9]

    Abeba Birhane, William Isaac, Vinodkumar Prabhakaran, Mark Diaz, Madeleine Clare Elish, Iason Gabriel, and Shakir Mohamed. 2022. Power to the People? Opportunities and Challenges for Participatory AI. InProceedings of the 2nd ACM Conference on Equity and Access in Algorithms, ...

    2022 doi

  2. [10]

    Rishi Bommasani, Sanjeev Arora, Jennifer Chayes, Yejin Choi, Mariano- Florentino Cuéllar, Li Fei-Fei, Daniel E Ho, Dan Jurafsky, Sanmi Koyejo, Hima Lakkaraju, et al. 2025. Advancing science-and evidence-based AI policy.Science 389, 6759 (2025), 459–461

    2025

  3. [11]

    Samuel R Bowman, Jeeyoon Hyun, Ethan Perez, Edwin Chen, Craig Pettit, Scott Heiner, Kamil˙e Lukoši¯ut˙e, Amanda Askell, Andy Jones, Anna Chen, et al. 2022. Measuring progress on scalable oversight for large language models.arXiv preprint arXiv:2211.03540(2022)

    2022 arXiv

  4. [12]

    Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology3, 2 (2006), 77–101

    2006

  5. [13]

    Scott Babwah Brennen and Matt Perault. 2023. The State of State Technology Policy: 2023 Report. https://library.naswa.org/doi/full/10.5555/20.500.11941/ 5356

    2023 doi

  6. [14]

    Matt Brown and Matt O’Brien. 2025. Senate pulls ai regulatory ban from GOP bill after complaints from states. https://www.pbs.org/newshour/politics/senate- pulls-ai-regulatory-ban-from-gop-bill-after-complaints-from-states

    2025

  7. [15]

    Thomas Davenport, Abhijit Guha, Dhruv Grewal, and Timna Bressgott. 2020. How artificial intelligence will change the future of marketing.Journal of the Academy of Marketing Science48, 1 (2020), 24–42

    2020

  8. [16]

    Janet Davis, Harry Hochheiser, Juan Pablo Hourcade, Jeff Johnson, Lisa Nathan, and Janice Tsai. 2012. Occupy CHI! engaging US policymakers. InCHI’12 Extended Abstracts on Human Factors in Computing Systems. 1139–1142

    2012

  9. [17]

    Tatevik Davtyan. 2025. The US approach to AI regulation: federal laws, policies, and strategies explained.Journal of Law, Technology, & the Internet16, 2 (2025), 223

    2025

  10. [18]

    Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert, and Tom Zick. 2021. Axes for sociotechnical inquiry in AI research.IEEE Transactions on Technology and Society2, 2 (2021), 62–70

    2021

  11. [19]

    2012.The social construction of technological systems, anniversary edition: New directions in the sociology and history of technology

    Deborah G Douglas. 2012.The social construction of technological systems, anniversary edition: New directions in the sociology and history of technology. MIT press

    2012

  12. [20]

    Noemi Dreksler, Harry Law, Chloe Ahn, Daniel Schiff, Kaylyn Jackson Schiff, and Zachary Peskowitz. 2025. What Does the Public Think About AI? An Overview of the Public’s Attitudes Towards AI and a Resource for Future Research.Centre for the Governance of AI(2025)

    2025

  13. [21]

    Brianna Dym, Namita Pasupuleti, and Casey Fiesler. 2022. Building a pillowfort: Political tensions in platform design and policy.Proceedings of the ACM on Human-Computer Interaction6, GROUP (2022), 1–23

    2022

  14. [22]

    Upol Ehsan, Koustuv Saha, Munmun De Choudhury, and Mark O Riedl. 2023. Charting the sociotechnical gap in explainable AI: A framework to address the gap in XAI.Proceedings of the ACM on human-computer interaction7, CSCW1 (2023), 1–32

    2023

  15. [23]

    Endersby, Mark L

    James W. Endersby, Mark L. Ritchey, and Nicholas L. Brothers. 2024. State Task Forces and Interest Groups. https://truman.missouri.edu/sites/default/files/ publication/endersby_posted_03.04.2025.pdf

    2024

  16. [24]

    Sophia Falk, David Ekchajzer, Thibault Pirson, Etienne Lees-Perasso, Augustin Wattiez, Lisa Biber-Freudenberger, Sasha Luccioni, and Aimee van Wynsberghe

  17. [25]

    arXiv:2509.00093 [cs.CY] https://arxiv.org/ abs/2509.00093 CHI, April 13–17, 2026, Barcelona, Spain Migineishvili et al

    More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU. arXiv:2509.00093 [cs.CY] https://arxiv.org/ abs/2509.00093 CHI, April 13–17, 2026, Barcelona, Spain Migineishvili et al

    2026

  18. [26]

    KJ Kevin Feng, Rock Yuren Pang, Tzu-Sheng Kuo, Amy Winecoff, Emily Tseng, David Gray Widder, Harini Suresh, Katharina Reinecke, and Amy X Zhang

  19. [27]

    In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

    Sociotechnical AI Governance: Challenges and Opportunities for HCI. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–6

  20. [28]

    Jennifer Fereday and Eimear Muir-Cochrane. 2006. Demonstrating rigor using thematic analysis: A hybrid approach of inductive and deductive coding and theme development.International journal of qualitative methods5, 1 (2006), 80–92

    2006

  21. [29]

    Emilio Ferrara. 2024. GenAI against humanity: Nefarious applications of gener- ative artificial intelligence and large language models.Journal of Computational Social Science7, 1 (2024), 549–569

    2024

  22. [30]

    Public Policy

    Ritika Gairola and Colin M Gray. 2025. How is" Public Policy" Used in HCI Scholarship?. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–8

    2025

  23. [31]

    Genesis Mission

    Elizabeth Gibney, Alexandra Witze, and Jenna Ahart. 2025. Trump’s Ai “Genesis Mission”: What are the risks and opportunities? https://www.nature.com/ articles/d41586-025-03890-z

    2025

  24. [32]

    Connie Golsteijn, Sarah Gallacher, Licia Capra, and Yvonne Rogers. 2015. Sens- Us: imagining a citizen-led, dynamic, and localized census. InProceedings of the 2015 British HCI Conference. 319–320

    2015

  25. [33]

    Gov.uk. [n. d.]. https://www.gov.uk/government/publications/ai-safety- summit-introduction/ai-safety-summit-introduction-html#ai-safety-summit- introduction

  26. [34]

    David H Guston. 1999. Evaluating the first US consensus conference: the impact of the citizens’ panel on telecommunications and the future of democracy. Science, Technology, & Human Values24, 4 (1999), 451–482

    1999

  27. [35]

    Philipp Hacker, Andreas Engel, and Marco Mauer. 2023. Regulating ChatGPT and other large generative AI models. InProceedings of the 2023 ACM conference on fairness, accountability, and transparency. 1112–1123

    2023

  28. [36]

    Sophia Hatz, Noemi Dreksler, Kevin Wei, and Baobao Zhang. 2025. Local US officials’ views on the impacts and governance of AI: Evidence from 2022 and 2023 survey waves.arXiv preprint arXiv:2501.09606(2025)

    2025

  29. [37]

    Amy K Heger, Liz B Marquis, Mihaela Vorvoreanu, Hanna Wallach, and Jennifer Wortman Vaughan. 2022. Understanding machine learning practitioners’ data documentation perceptions, needs, challenges, and desiderata.Proceedings of the ACM on Human-Computer Interaction6, CSCW2 (2022), 1–29

    2022

  30. [38]

    Austin Henderson. 1998. HCI/SIGCHI issues for Policy’98.ACM SIGCHI Bulletin 30, 4 (1998), 75–76

    1998

  31. [39]

    Michael C Horowitz. 2016. Public opinion and the politics of the killer robots debate.Research & Politics3, 1 (2016), 2053168015627183

    2016

  32. [40]

    The White House. 2025. Fact sheet: President Donald J. Trump unveils the genesis mission to accelerate AI for scientific discovery. https: //www.whitehouse.gov/fact-sheets/2025/11/fact-sheet-president-donald-j- trump-unveils-the-genesis-missionto-accelerate-ai-for-scientific-d...

    2025

  33. [41]

    C Jackson. 2025. Google/Ipsos Multi-Country AI Survey 2025.Ipsos, Washington, DC, Tech. Rep(2025)

    2025

  34. [42]

    Steven J Jackson, Tarleton Gillespie, and Sandy Payette. 2014. The policy knot: Re-integrating policy, practice and design in CSCW studies of social computing. InProceedings of the 17th ACM conference on Computer supported cooperative work & social computing. 588–602

    2014

  35. [43]

    Joost Jongerden. 2008. First the peasant? Some reflections on modernity, technol- ogy and reconstruction.Reconstructing biotechnologies: Critical social analyses (2008), 119–142

    2008

  36. [44]

    Lynn H Kaack, Priya L Donti, Emma Strubell, George Kamiya, Felix Creutzig, and David Rolnick. 2022. Aligning artificial intelligence with climate change mitigation.Nature Climate Change12, 6 (2022), 518–527

    2022

  37. [45]

    Pratyusha R. Kalluri. 2024.Inside Ai: How Artificial Intelligence is Concentrating Power. Ph. D. Dissertation. Stanford University. https://www.proquest.com/dissertations-theses/inside-ai-how-artificial- intelligence-is/docview/3161891490/se-2 Copyright - Database copyright Pr...

    2024

  38. [46]

    Margot E Kaminski. 2023. Regulating the Risks of AI.BUL Rev.103 (2023), 1347

    2023

  39. [47]

    PM Krafft, Meg Young, Michael Katell, Jennifer E Lee, Shankar Narayan, Micah Epstein, Dharma Dailey, Bernease Herman, Aaron Tam, Vivian Guetler, et al

  40. [48]

    InProceedings of the 2021 ACM conference on fairness, accountability, and transparency

    An action-oriented AI policy toolkit for technology audits by community advocates and activists. InProceedings of the 2021 ACM conference on fairness, accountability, and transparency. 772–781

    2021

  41. [49]

    David Kriebel, Joel Tickner, Paul Epstein, John Lemons, Richard Levins, Edward L Loechler, Margaret Quinn, Ruthann Rudel, Ted Schettler, and Michael Stoto

  42. [50]

    The precautionary principle in environmental science.Environmental health perspectives109, 9 (2001), 871–876

    2001

  43. [51]

    Monika Kurath and Priska Gisler. 2009. Informing, involving or engaging? Science communication, in the ages of atom-, bio-and nanotechnology.Public Understanding of Science18, 5 (2009), 559–573

    2009

  44. [52]

    Jonathan Lazar. 2015. Public policy and HCI: making an impact in the future. Interactions22, 5 (2015), 69–71

    2015

  45. [53]

    Joseph Lee. 2020. Access to finance for artificial intelligence regulation in the financial services industry.European Business Organization Law Review21, 4 (2020), 731–757

    2020

  46. [54]

    Nicol Turner Lee. 2024. Why states and localities are acting on AI. https: //www.brookings.edu/articles/why-states-and-localities-are-acting-on-ai/

    2024

  47. [55]

    Liang, Sean A

    Calvin A. Liang, Sean A. Munson, and Julie A. Kientz. 2021. Embracing Four Tensions in Human-Computer Interaction Research with Marginalized People. ACM Trans. Comput.-Hum. Interact.28, 2, Article 14 (apr 2021), 47 pages. doi:10. 1145/3443686

    2021

  48. [56]

    Valerio Lubello. 2025. From Biden to Trump: Divergent and Convergent Policies in The Artificial Intelligence (AI) Summer.A vailable at SSRN 5302544(2025)

    2025

  49. [57]

    Alexandra Sasha Luccioni, Emma Strubell, and Kate Crawford. 2025. From efficiency gains to rebound effects: The problem of jevons’ paradox in AI’s polarized environmental debate. InProceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. 76–88

    2025

  50. [58]

    Aale Luusua and Johanna Ylipulli. 2020. Artificial intelligence and risk in design. InProceedings of the 2020 ACM designing interactive systems conference. 1235–1244

    2020

  51. [59]

    Matthijs M Maas. 2023. AI is like. . . A literature review of AI metaphors and why they matter for policy.AI Foundations Report2 (2023)

    2023

  52. [60]

    Matthijs M Maas. 2023. Concepts in advanced AI governance: A literature review of key terms and definitions.AI Foundations Report3 (2023)

    2023

  53. [61]

    Atefeh Mahdavi Goloujeh, Anne Sullivan, and Brian Magerko. 2024. The so- cial construction of generative ai prompts. InExtended Abstracts of the CHI Conference on Human Factors in Computing Systems. 1–7

    2024

  54. [62]

    Jennifer Manuel and Clara Crivellaro. 2020. Place-based policymaking and HCI: Opportunities and challenges for technology design. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–16

    2020

  55. [63]

    Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, et al. 2025. Artificial intelligence index report 2025.arXiv preprint arXiv:2504.07139(2025)

    2025

  56. [64]

    Taylor Meek, Husam Barham, Nader Beltaif, Amani Kaadoor, and Tanzila Akhter

  57. [65]

    In2016 Portland International Conference on Management of Engineering and Technology (PICMET)

    Managing the ethical and risk implications of rapid advances in artificial intelligence: A literature review. In2016 Portland International Conference on Management of Engineering and Technology (PICMET). IEEE, 682–693

  58. [66]

    Nino Migineishvili, Madeleine Grunde-McLaughlin, Emmanuel Azuh, Spencer A Wood, Rene Just, and Katharina Reinecke. 2025. Wildfire and Forest Manage- ment: Opportunities for HCI Research.ACM Transactions on Computer-Human Interaction(2025)

    2025

  59. [67]

    Gustavo Moreira, Edyta Paulina Bogucka, Marios Constantinides, and Daniele Quercia. 2025. The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the US Public Through an Interactive Plat- form. InProceedings of the 2025 CHI Conference on ...

    2025

  60. [68]

    NCSL. 2025. https://www.ncsl.org/center-for-legislative-strengthening/public- records-law-and-state-legislatures

    2025

  61. [69]

    Maria Nordström. 2022. AI under great uncertainty: implications and decision strategies for public policy.AI & society37, 4 (2022), 1703–1714

    2022

  62. [70]

    Anthony J Nownes and Patricia Freeman. 1998. Interest group activity in the states.The Journal of Politics60, 1 (1998), 86–112

    1998

  63. [71]

    Johanna Nuojua, Antti Juustila, Toni Räisänen, Kari Kuutti, and Leena Soudun- saari. 2008. Exploring web-based participation methods for urban planning.. In PDC. 274–277

    2008

  64. [72]

    Olubusola Odeyemi, Oluwafunmi Adijat Elufioye, Noluthando Zamanjomane Mhlongo, and Andrew Ifesinachi. 2024. AI in E-commerce: Reviewing develop- ments in the USA and their global influence.International Journal of Science and Research Archive11, 1 (2024), 1460–1468

    2024

  65. [73]

    Serena Oduro. 2025. Shaping AI standards to protect America’s most vulnera- ble: Tech innovators. https://www.techpolicy.press/shaping-ai-standards-to- protect-americas-most-vulnerable-tech-innovators/

    2025

  66. [74]

    Future of Privacy Forum. 2025. . https://fpf.org/multistate-ai-policymaker- working-group/

    2025

  67. [75]

    Executive Office of the President. [n. d.]. Federal Register. https://www. federalregister.gov/documents/full_text/html/2025/01/31/2025-02172.html

    2025

  68. [76]

    Executive Office of the President. 2022. GovInfo. https://www.govinfo.gov/ content/pkg/CFR-2024-title3-vol1/pdf/CFR-2024-title3-vol1-eo14110.pdf

    2022

  69. [77]

    Mimi Onuoha and Diana Nucera. 2022. A People’s Guide to Tech. https: //alliedmedia.org/resources/peoples-guide-to-ai

    2022

  70. [78]

    Matt O’Brien. 2025. CHATGPT chief says artificial intelligence should be regulated by a US or Global Agency. https://apnews.com/article/chatgpt- openai-ceo-sam-altman-congress-73ff96c6571f38ad5fd68b3072722790

    2025

  71. [79]

    Annie Palmer. 2025. Ai will shrink Amazon’s workforce in the coming years, CEO Jassy says. https://www.cnbc.com/2025/06/17/ai-amazon-workforce- jassy.html

    2025

  72. [80]

    Srinivas Parinandi, Jesse Crosson, Kai Peterson, and Sinan Nadarevic. 2024. Investigating the politics and content of US State artificial intelligence legislation. Business and Politics26, 2 (2024), 240–262. Regulating AI: Where U.S. State Policy and HCI (Mis)align CHI, April ...

    2024

  73. [81]

    Sean Peacock, Robert Anderson, and Clara Crivellaro. 2018. Streets for peo- ple: engaging children in placemaking through a socio-technical process. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1–14

    2018

  74. [82]

    Mark Penn, Dritan Nesho, and Stephen Ansolabehere. [n. d.]. https: //harvardharrispoll.com/wp-content/uploads/2023/07/HHP_July2023_ KeyResults.pdf

    2023

  75. [83]

    Billy Perrigo. 2023. OpenAI used Kenyan workers on less than $2 per hour: Exclusive. https://time.com/6247678/openai-chatgpt-kenya-workers/

    2023

  76. [84]

    Roxana Radu. 2021. Steering the governance of artificial intelligence: national strategies in perspective.Policy and society40, 2 (2021), 178–193

    2021

  77. [85]

    Lee Rainie, Cary Funk, Monica Anderson, and Alec Tyson. 2022. AI and Human Enhancement: American’s Openness Is Tempered by a Range of Concerns: Public views are tied to how these technologies would be used, what constraints would be in place. (2022). https://www.pewresearch. o...

    2022

  78. [86]

    Lena Recki, Dennis Lawo, Veronika Krauß, Dominik Pins, and Alexander Boden

  79. [87]

    You Can either Blame Technology or Blame a Person

    " You Can either Blame Technology or Blame a Person... "—A Conceptual Model of Users’ AI-Risk Perception as a Tool for HCI.Proceedings of the ACM on Human-Computer Interaction8, CSCW2 (2024), 1–25

    2024

  80. [88]

    Sandeep Reddy, Sonia Allan, Simon Coghlan, and Paul Cooper. 2020. A gover- nance model for the application of AI in health care.Journal of the American medical informatics association27, 3 (2020), 491–497

    2020

  81. [89]

    Anka Reuel, Lisa Soder, Ben Bucknall, and Trond Arne Undheim. 2024. Position paper: Technical research and talent is needed for effective AI governance.arXiv preprint arXiv:2406.06987(2024)

    2024 arXiv

  82. [90]

    Eva Roytburg. 2024. Google has walked back on its carbon-neutral promise due to energy-gobbling AI. https://fortune.com/2024/07/10/ai-has-destroyed- googles-promise-of-carbon-neutrality-with-emissions-rising-50-over-the- last-five-years/

    2024

  83. [91]

    2014.Theories of the policy process

    Paul A Sabatier and Christopher Weible. 2014.Theories of the policy process. Westview press

    2014

  84. [92]

    Alexander K Saeri, Sophia Lloyd George, Jess Graham, Clelia Lacar- riere, Peter Slattery, and Neil Thompson. 2025. Mapping AI Risk Mitigation: Evidence Scan and Draft Mitigation Taxonomy. (2025). https://cdn.prod.website-files.com/669550d38372f33552d2516e/ 6887e58496902e3bcad0...

    2025

  85. [93]

    Ali Mohammad Saghiri, S Mehdi Vahidipour, Mohammad Reza Jabbarpour, Mehdi Sookhak, and Agostino Forestiero. 2022. A survey of artificial intelligence challenges: Analyzing the definitions, relationships, and evolutions.Applied sciences12, 8 (2022), 4054

    2022

  86. [94]

    Conrad Sanderson, David Douglas, Qinghua Lu, Emma Schleiger, Jon Whittle, Justine Lacey, Glenn Newnham, Stefan Hajkowicz, Cathy Robinson, and David Hansen. 2023. AI ethics principles in practice: Perspectives of designers and developers.IEEE Transactions on Technology and Soci...

    2023

  87. [95]

    human-AI collaboration

    Advait Sarkar. 2023. Enough with “human-AI collaboration”. InExtended ab- stracts of the 2023 CHI conference on human factors in computing systems. 1–8

    2023

  88. [96]

    Daniel Schiff, Justin Biddle, Jason Borenstein, and Kelly Laas. 2020. What’s next for ai ethics, policy, and governance? a global overview. InProceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 153–158

    2020

  89. [97]

    Daniel Schiff, Jason Borenstein, Justin Biddle, and Kelly Laas. 2021. AI ethics in the public, private, and NGO sectors: A review of a global document collection. IEEE Transactions on Technology and Society2, 1 (2021), 31–42

    2021

  90. [98]

    Renee Shelby, Shalaleh Rismani, Kathryn Henne, AJung Moon, Negar Ros- tamzadeh, Paul Nicholas, N’Mah Yilla-Akbari, Jess Gallegos, Andrew Smart, Emilio Garcia, et al. 2023. Sociotechnical harms of algorithmic systems: Scoping a taxonomy for harm reduction. InProceedings of the ...

    2023

  91. [99]

    Eli Sherman and Ian Eisenberg. 2024. Ai risk profiles: A standards proposal for pre-deployment ai risk disclosures. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 23047–23052

    2024

  92. [100]

    Peter Slattery, Alexander K Saeri, Emily AC Grundy, Jess Graham, Michael Noetel, Risto Uuk, James Dao, Soroush Pour, Stephen Casper, and Neil Thompson

  93. [101]

    The ai risk repository: A comprehensive meta-review, database, and taxonomy of risks from artificial intelligence.arXiv preprint arXiv:2408.12622 (2024)

    2024 arXiv

  94. [102]

    Alicia Solow-Niederman. 2023. Can AI standards have politics?UCLA L. Rev. Discourse71 (2023), 230

    2023

  95. [103]

    Aaron Sorkin, Ravi Mattu, Bernhard Warner, Michael de la Merced, Lauren Hirsch, and Ephrat Livni. 2023. Why Lawmakers Aren’t Rushing to Police A.I

    2023

  96. [104]

    Anne Spaa, Abigail Durrant, Chris Elsden, and John Vines. 2019. Understanding the Boundaries between Policymaking and HCI. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–15

    2019

  97. [105]

    Reem Talhouk, Kellie Morrissey, Sarah Fox, Nadia Pantidi, Emma Simpson, Lydia Emma Michie, and Madeline Balaam. 2018. Human computer interaction & health activism. InExtended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. 1–4

    2018

  98. [106]

    Sónia Teixeira, José Rodrigues, Bruno Veloso, and João Gama. 2022. An Ex- ploratory Diagnosis of Artificial Intelligence Risks for a Responsible Gover- nance.. InProceedings of the 15th International Conference on Theory and Practice of Electronic Governance. 25–31

    2022

  99. [107]

    David R Thomas. 2006. A general inductive approach for analyzing qualitative evaluation data.American journal of evaluation27, 2 (2006), 237–246

    2006

  100. [108]

    Vanessa Thomas, Christian Remy, Mike Hazas, and Oliver Bates. 2017. HCI and environmental public policy: Opportunities for engagement. InProceedings of the 2017 CHI conference on human factors in computing systems. 6986–6992

    2017

  101. [109]

    Gael Varoquaux, Sasha Luccioni, and Meredith Whittaker. 2025. Hype, Sus- tainability, and the Price of the Bigger-is-Better Paradigm in AI. InProceed- ings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’25). Association for Computing Machinery...

    2025 doi

  102. [110]

    Stepan Vesely and Byungdoo Kim. 2024. Survey evidence on public support for AI safety oversight.Scientific Reports14, 1 (2024), 31491

    2024

  103. [111]

    Yilun Wang and Michal Kosinski. 2018. Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.Journal of personality and social psychology114, 2 (2018), 246

    2018

  104. [112]

    Graham Webster, Jason Zhou, Mingli Shi, Hunter Dorwart, Johanna Costigan, and Qiheng Chen. 2023. Forum: Analyzing an expert proposal for China’s Arti- ficial Intelligence Law. https://digichina.stanford.edu/work/forum-analyzing- an-expert-proposal-for-chinas-artificial-intelli...

    2023

  105. [113]

    Redmiles, and Franziska Roesner

    Miranda Wei, Jaron Mink, Yael Eiger, Tadayoshi Kohno, Elissa M. Redmiles, and Franziska Roesner. 2024. SoK (or SoLK?): On the Quantitative Study of Sociode- mographic Factors and Computer Security Behaviors. arXiv:2404.10187 https:// www.usenix.org/conference/usenixsecurity24/...

    2024 arXiv

  106. [114]

    James Weichert, Qin Zhu, Dayoung Kim, and Hoda Eldardiry. 2025. Perceptions of AI Ethics Policies Among Scientists and Engineers in Policy-Related Roles: An Exploratory Investigation.Digital Society4, 1 (2025), 1–25

    2025

  107. [115]

    Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, et al

  108. [116]

    InProceedings of the 2022 ACM conference on fairness, accountability, and transparency

    Taxonomy of risks posed by language models. InProceedings of the 2022 ACM conference on fairness, accountability, and transparency. 214–229

    2022

  109. [117]

    EMMA WITHROW. 2025. Fact check team: Exploring AI Data Centers’ impact on U.S. resources. https://katv.com/news/nation-world/fact-check- team-exploring-ai-data-centers-impact-on-us-resources-power-grids- pew-research-center-innovation-energy-water-systems-environmental- challenges

    2025

  110. [118]

    Richmond Y Wong and Steven J Jackson. 2015. Wireless visions: Infrastructure, imagination, and US spectrum policy. InProceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. 105–115

    2015

  111. [119]

    Edward J Woodhouse. 2005. (Re) constructing technological society by taking social construction even more seriously.Social epistemology19, 2-3 (2005), 199–223

    2005

  112. [120]

    Allison Woodruff, Renee Shelby, Patrick Gage Kelley, Steven Rousso-Schindler, Jamila Smith-Loud, and Lauren Wilcox. 2024. How knowledge workers think generative ai will (not) transform their industries. InProceedings of the 2024 CHI Conference on Human Factors in Computing Sys...

    2024

  113. [121]

    Sally Wyatt. 2023. Technological determinism: what it is and why it matters. In Technology ethics. Routledge, 26–33

    2023

  114. [122]

    Shixian Xie, John Zimmerman, and Motahhare Eslami. 2025. Exploring What People Need to Know to be AI Literate: Tailoring for a Diversity of AI Roles and Responsibilities. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–16

    2025

  115. [123]

    Qian Yang, Aaron Steinfeld, and John Zimmerman. 2019. Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes. InProceedings of the 2019 CHI Conference on Human Factors in Com- puting Systems(Glasgow, Scotland Uk)(CHI ’19). Assoc...

    2019 doi

  116. [124]

    Qian Yang, Richmond Y Wong, Steven Jackson, Sabine Junginger, Margaret D Hagan, Thomas Gilbert, and John Zimmerman. 2024. The future of HCI-policy collaboration. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–15

    2024

  117. [125]

    Meg Young, Lassana Magassa, and Batya Friedman. 2019. Toward inclusive tech policy design: a method for underrepresented voices to strengthen tech policy documents.Ethics and Information Technology21, 2 (2019), 89–103. A Mixed-Effects Logistic Regression We conducted a paired ...

    2019

Pith tools

Reviewed July 12, 2026 · model on record in the stance chip above.