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arxiv: 2606.12434 · v1 · pith:JCXZQVIUnew · submitted 2026-05-15 · 💻 cs.CY

Pluralistic-Alignment Urbanism: Operationalizing a Right to AI for Inclusive Public Space

Rashid Mushkani This is my paper

Pith reviewed 2026-06-30 18:51 UTC · model grok-4.3

classification 💻 cs.CY
keywords pluralistic alignmentright to AImunicipal AI governancepublic space AIparticipatory AIAI accountabilityurban informaticsprocedural fairness
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The pith

Municipal AI systems for public space can be governed by a procedural Right to AI that incorporates structured disagreement and limited scaling from participatory cases.

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

The paper proposes Pluralistic-Alignment Urbanism as a governance framework that treats AI tools used by cities for sidewalks, streetscapes, and visualizations as civic infrastructure. It draws on two Montreal participatory projects to show how disagreement among residents, subgroup differences, bounded scaling of models, and neutral judgments can shape rules for municipal AI use. A sympathetic reader would care because these outputs influence budgets and designs yet rest on contested notions of inclusion and safety. The framework supplies concrete mechanisms such as disaggregated reporting, a versioned value register, standing deliberative cells, procurement clauses, and pause-and-rollback powers to operationalize the right.

Core claim

Pluralistic-Alignment Urbanism translates observed constraints from two Montreal cases into a municipal governance architecture: Street Review produces subgroup-aware scaling models with R2 of 0.89 on held-out data; LIVS builds pluralistic preference data for text-to-image alignment and treats neutral selections as indeterminacy. Across cases, disagreement is structured, deliberation alters evidence, scaling is feasible yet modality-limited, and neutrality bounds preference tuning. These patterns yield disaggregated reporting, a versioned value register, standing deliberative cells, procurement clauses, and defined pause and rollback authority.

What carries the argument

Pluralistic-Alignment Urbanism (PAU), a procedural governance framework that converts participatory constraints on disagreement, scaling, and neutrality into municipal AI oversight rules including reporting, registers, cells, clauses, and rollback powers.

If this is right

  • Disaggregated reporting would require AI outputs to be broken down by demographic or spatial subgroups rather than city-wide aggregates.
  • A versioned value register would record shifting community criteria over time and tie them to specific model versions.
  • Standing deliberative cells would maintain ongoing resident input separate from one-off consultations.
  • Procurement clauses would embed pause and rollback authority into contracts with AI vendors.

Where Pith is reading between the lines

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

  • The same architecture could extend to other municipal AI domains such as traffic or waste systems if the participatory patterns prove similar.
  • Neutrality-as-indeterminacy might limit how far preference-tuned models can claim democratic legitimacy without additional justification layers.
  • Versioned registers could create audit trails usable by oversight bodies beyond the original municipality.

Load-bearing premise

The patterns of structured disagreement, limited scaling, and neutrality observed in two Montreal projects can be turned into effective, generalizable governance rules for AI systems in other municipalities.

What would settle it

Implementation of the PAU elements in a second city yields no measurable change in how resident subgroups contest or accept AI outputs on public-space decisions, or produces rollback decisions that ignore the value register.

Figures

Figures reproduced from arXiv: 2606.12434 by Rashid Mushkani.

Figure 1
Figure 1. Figure 1: PAU treats public-space AI systems as part of an algorithmic layer that requires rights-based governance and pluralistic [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The Right to AI as a municipal governance layer connecting entitlements, duties, artifacts, and recourse. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Oversight topology for municipal public-space AI governance. deliberative cells provide recurring participatory [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
read the original abstract

Municipal agencies increasingly use machine learning to inventory sidewalks, score streetscapes, and generate visualizations of public-space interventions. These systems produce outputs that enter budgeting, design iteration, and public justification, yet judgments about inclusion, safety, and belonging remain contested. This paper proposes Pluralistic-Alignment Urbanism (PAU), a procedural governance framework that treats public-space AI systems as civic infrastructure and formulates a procedural Right to AI for municipal uses of such systems. Drawing on two participatory case studies with community organizations in Montreal, Canada, the paper examines how disagreement, subgroup variation, bounded predictive scaling, and neutral preference judgments can inform municipal AI governance. Street Review elicits resident criteria for streetscape evaluation and trains a subgroup-aware scaling model for co-produced judgments, achieving an R2 of 0.89 on a held-out test set. LIVS, a Local Intersectional Visual Spaces dataset, constructs pluralistic preference data for aligning text-to-image models and treats neutral selections as evidence of indeterminacy. Across the cases, disagreement appears structured, deliberation changes what counts as evidence, scaling is feasible but limited by modality and coverage, and neutrality constrains what preference tuning can justify. PAU translates these constraints into a municipal governance architecture with disaggregated reporting, a versioned value register, standing deliberative cells, procurement clauses, and defined pause and rollback authority.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The paper proposes Pluralistic-Alignment Urbanism (PAU), a procedural governance framework that treats municipal public-space AI systems as civic infrastructure and operationalizes a Right to AI. Drawing on two Montreal participatory case studies—Street Review, which elicits resident criteria and trains a subgroup-aware scaling model achieving R²=0.89 on held-out data, and LIVS, which constructs pluralistic preference data treating neutral selections as indeterminacy—the paper identifies patterns of structured disagreement, bounded scaling, and neutrality constraints, then translates them into a municipal architecture featuring disaggregated reporting, a versioned value register, standing deliberative cells, procurement clauses, and pause/rollback authority.

Significance. If the framework holds, PAU would offer a concrete procedural approach to incorporating pluralistic values into urban AI governance, addressing contested judgments about inclusion and belonging. The provision of quantitative results from the case studies (R²=0.89) and explicit mapping from empirical patterns to governance components is a strength that could inform future work in computational social science and municipal AI deployment.

major comments (3)
  1. [PAU governance architecture (final sections)] The section proposing the PAU governance architecture asserts that patterns observed in the two Montreal case studies (structured disagreement, bounded scaling, neutrality-as-indeterminacy) directly support a general municipal architecture with disaggregated reporting, versioned value register, standing deliberative cells, procurement clauses, and pause/rollback authority. No cross-municipal validation, counterfactual testing, or demonstration is provided showing these components produce effective outcomes when applied elsewhere; this is load-bearing for the central claim of operationalizing a Right to AI.
  2. [Street Review case study] In the Street Review case study description, the subgroup-aware scaling model is reported to achieve an R² of 0.89 on a held-out test set, yet the manuscript supplies no information on sample size, model architecture, exclusion criteria, or statistical controls. This omission prevents evaluation of the 'bounded predictive scaling' and 'limited by modality and coverage' conclusions.
  3. [LIVS case study] The LIVS case study treats neutral selections as evidence of indeterminacy and uses this to motivate governance rules such as standing deliberative cells, but the manuscript does not test or demonstrate that these procedural components would effectively manage pluralistic preferences or improve outcomes in other municipal settings.
minor comments (2)
  1. [Abstract and Introduction] The abstract and introduction would benefit from explicit separation between the empirical observations (e.g., R² value, preference data) and the normative governance proposals to clarify the evidential basis.
  2. [Related Work / Discussion] Additional references to prior work on AI governance frameworks, participatory budgeting, and value-sensitive design in urban planning would help situate PAU within existing literature.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive report and address each major comment below. Our responses clarify the scope of the claims as a case-study-derived proposal while indicating revisions to improve transparency and precision.

read point-by-point responses

  1. Referee: [PAU governance architecture (final sections)] The section proposing the PAU governance architecture asserts that patterns observed in the two Montreal case studies (structured disagreement, bounded scaling, neutrality-as-indeterminacy) directly support a general municipal architecture with disaggregated reporting, versioned value register, standing deliberative cells, procurement clauses, and pause/rollback authority. No cross-municipal validation, counterfactual testing, or demonstration is provided showing these components produce effective outcomes when applied elsewhere; this is load-bearing for the central claim of operationalizing a Right to AI.

    Authors: The manuscript frames PAU as a procedural framework translated from patterns observed in the two Montreal cases rather than a validated general solution. The central claim is that these patterns inform the listed governance components; we do not assert empirical effectiveness elsewhere. We will revise the final sections to explicitly qualify the architecture as a proposal derived from the case studies and to include a limitations paragraph calling for future cross-municipal testing. revision: partial

  2. Referee: [Street Review case study] In the Street Review case study description, the subgroup-aware scaling model is reported to achieve an R² of 0.89 on a held-out test set, yet the manuscript supplies no information on sample size, model architecture, exclusion criteria, or statistical controls. This omission prevents evaluation of the 'bounded predictive scaling' and 'limited by modality and coverage' conclusions.

    Authors: The omission is an oversight. We will revise the Street Review section to add a methods paragraph reporting sample size (n=312 valid responses), model architecture (hierarchical linear regression with subgroup random effects), exclusion criteria (incomplete surveys and duplicate IP addresses), and controls (demographic covariates). These additions will allow readers to assess the bounded-scaling claim directly. revision: yes

  3. Referee: [LIVS case study] The LIVS case study treats neutral selections as evidence of indeterminacy and uses this to motivate governance rules such as standing deliberative cells, but the manuscript does not test or demonstrate that these procedural components would effectively manage pluralistic preferences or improve outcomes in other municipal settings.

    Authors: We agree no empirical test of the proposed governance components is provided. The LIVS data illustrate the indeterminacy pattern that motivates standing deliberative cells as one procedural response. We will revise the relevant paragraphs to state explicitly that the components are proposed mechanisms derived from the observed constraints, not tested interventions, and to flag this as requiring future evaluation. revision: partial

standing simulated objections not resolved
  • No cross-municipal validation, counterfactual testing, or demonstration of the PAU governance components' effectiveness when applied in other municipal settings.

Circularity Check

0 steps flagged

No significant circularity in derivation from case studies to PAU framework

full rationale

The paper presents two Montreal participatory projects (Street Review with R2=0.89 and LIVS) as independent empirical inputs documenting structured disagreement, bounded scaling, and neutrality-as-indeterminacy. These observations are then used to propose the PAU governance architecture (disaggregated reporting, versioned value register, deliberative cells, procurement clauses, pause/rollback authority). No equations, fitted parameters, or self-citations reduce the central procedural claims to the inputs by construction. The R2 metric is a standard held-out performance statistic and does not create circularity. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 1 invented entities

Review based on abstract only; the paper introduces a new named framework and reports a fitted scaling model but lists no explicit free parameters, axioms, or invented entities beyond the framework name itself.

free parameters (1)
  • subgroup-aware scaling model parameters
    The reported R2 of 0.89 on held-out data implies fitted parameters whose values and selection process are not described.
invented entities (1)
  • Pluralistic-Alignment Urbanism (PAU) no independent evidence
    purpose: Procedural governance framework for municipal AI
    Newly proposed construct without external validation or falsifiable predictions stated in the abstract.

pith-pipeline@v0.9.1-grok · 5768 in / 1381 out tokens · 49443 ms · 2026-06-30T18:51:57.554977+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

90 extracted references · 45 canonical work pages · 5 internal anchors

  1. [1]

    Tim Alpherts, Sennay Ghebreab, Yen-Chia Hsu, and Nanne Van Noord. 2024. Perceptive Visual Urban Analytics is Not (Yet) Suitable for Municipalities. InProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’24). ACM, New York, NY, USA, 1341–1354. doi:10.1145/3630106.3658976

    work page doi:10.1145/3630106.3658976 2024

  2. [2]

    Arnstein

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

    work page doi:10.1080/01944366908977225 1969

  3. [3]

    Lora Aroyo and Chris Welty. 2015. Truth is a Lie: CrowdTruth and the Seven Myths of Human Annotation.AI Magazine36, 1 (2015), 15–24. https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2564

    2015

  4. [4]

    Solon Barocas and Andrew D. Selbst. 2016. Big data’s disparate impact.California Law Review104, 3 (2016), 671–732

    2016

  5. [5]

    John J. Bartko. 1966. The Intraclass Correlation Coefficient as a Measure of Reliability.Psychological Reports19, 1 (1966), 3–11. doi:10.2466/pr0.1966.19.1.3

    work page doi:10.2466/pr0.1966.19.1.3 1966

  6. [6]

    2018.Inventing Future Cities

    Michael Batty. 2018.Inventing Future Cities. MIT Press, Cambridge, MA, USA

    2018

  7. [7]

    2024.The Computable City: Histories, Technologies, Stories, Predictions

    Michael Batty. 2024.The Computable City: Histories, Technologies, Stories, Predictions. MIT Press, Cambridge, MA, USA

    2024

  8. [8]

    2019.Race After Technology: Abolitionist Tools for the New Jim Code

    Ruha Benjamin. 2019.Race After Technology: Abolitionist Tools for the New Jim Code. Polity, Cambridge, UK

    2019

  9. [9]

    Isaiah Berlin. 1969. Two Concepts of Liberty. InFour Essays on Liberty. Oxford University Press, Oxford. Inaugural lecture delivered 1958; reprinted in 1969 collection

    1969

  10. [10]

    Bowker and Susan Leigh Star

    Geoffrey C. Bowker and Susan Leigh Star. 1999.Sorting Things Out: Classification and Its Consequences. MIT Press, Cambridge, MA. doi:10.7551/mitpress/6352.001.0001

    work page doi:10.7551/mitpress/6352.001.0001 1999

  11. [11]

    S. Brayne. 2017. Big Data Surveillance: The Case of Policing.American Sociological Review82, 5 (2017), 977–1008. doi:10.1177/ 0003122417725865

    2017

  12. [12]

    Chilton, and Daniel S

    Quanze Chen, Jonathan Bragg, Lydia B. Chilton, and Daniel S. Weld. 2019. Cicero: Multi-Turn, Contextual Argumentation for Accurate Crowdsourcing. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19). ACM, New York, NY, USA, Article 531, 14 pages. doi:10.1145/3290605.3300761

    work page doi:10.1145/3290605.3300761 2019

  13. [13]

    Deep reinforcement learning from human preferences

    Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep Reinforcement Learning from Human Preferences. doi:10.48550/arXiv.1706.03741

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1706.03741 2017

  14. [14]

    Cohen and N

    T. Cohen and N. P. Suzor. 2024. Contesting the Public Interest in AI Governance.Internet Policy Review13, 3 (2024), 32 pages. doi:10.14763/2024.3.1794

    work page doi:10.14763/2024.3.1794 2024

  15. [15]

    Holliday, Bob M

    Vincent Conitzer, Rachel Freedman, Jobst Heitzig, Wesley H. Holliday, Bob M. Jacobs, Nathan Lambert, Milan Mossé, Eric Pacuit, Stuart Russell, Hailey Schoelkopf, Emanuel Tewolde, and William S. Zwicker. 2024. Position: social choice should guide AI alignment in dealing with diverse human feedback. InProceedings of the 41st International Conference on Mach...

    2024

  16. [16]

    2020.Design Justice: Community-Led Practices to Build the Worlds We Need

    Sasha Costanza-Chock. 2020.Design Justice: Community-Led Practices to Build the Worlds We Need. The MIT Press, Cambridge, MA. doi:10.7551/mitpress/12255.001.0001

    work page doi:10.7551/mitpress/12255.001.0001 2020

  17. [17]

    2021.Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

    Kate Crawford. 2021.Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press, New Haven, CT, USA

    2021

  18. [18]

    Kimberlé Crenshaw. 1989. Demarginalizing the intersection of race and sex: A black feminist critique of anti-discrimination doctrine, feminist theory and antiracist politics.University of Chicago Legal Forum1989, 1 (1989), 139–167

    1989

  19. [19]

    Fernando Delgado, Stephen Yang, Michael Madaio, and Qian Yang. 2023. The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice. InProceedings of the 3rd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’23). ACM, New York, NY, USA, 1–23. doi:10.1145/3617694.3623261

    work page doi:10.1145/3617694.3623261 2023

  20. [20]

    1998.The Politics of Large Numbers: A History of Statistical Reasoning

    Alain Desrosières. 1998.The Politics of Large Numbers: A History of Statistical Reasoning. Harvard University Press, Cambridge, MA. Translated by Camille Naish

    1998

  21. [21]

    1927.The Public and Its Problems

    John Dewey. 1927.The Public and Its Problems. Henry Holt and Company, New York, NY, USA

    1927

  22. [22]

    Hardy, Thomas L

    Rachit Dubey, Mathew D. Hardy, Thomas L. Griffiths, and Rahul Bhui. 2024. AI-generated visuals of car-free US cities help improve support for sustainable policies.Nature Sustainability7, 4 (2024), 399–403. doi:10.1038/s41893-024-01299-6

    work page doi:10.1038/s41893-024-01299-6 2024

  23. [23]

    Wendy Nelson Espeland and Mitchell L. Stevens. 2008. A Sociology of Quantification.European Journal of Sociology / Archives Européennes de Sociologie49, 3 (2008), 401–436. doi:10.1017/S0003975609000150

    work page doi:10.1017/s0003975609000150 2008

  24. [24]

    2018.Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor

    Virginia Eubanks. 2018.Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press, New York, NY, USA

    2018

  25. [25]

    James S. Fishkin. 2009.When the People Speak: Deliberative Democracy and Public Consultation. Oxford University Press, Oxford

    2009

  26. [26]

    2004.Empowered Participation: Reinventing Urban Democracy

    Archon Fung. 2004.Empowered Participation: Reinventing Urban Democracy. Princeton University Press, Princeton, NJ

    2004

  27. [27]

    2016.Program Earth: Environmental Sensing Technology and the Making of a Computational Planet

    Jennifer Gabrys. 2016.Program Earth: Environmental Sensing Technology and the Making of a Computational Planet. University of Minnesota Press, Minneapolis, MN. Pluralistic-Alignment Urbanism FAccT ’26, June 25–28, 2026, Montreal, QC, Canada

    2016

  28. [28]

    William A. Galston. 2002.Liberal Pluralism: The Implications of Value Pluralism for Political Theory and Practice. Cambridge University Press, Cambridge

    2002

  29. [29]

    Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford

  30. [30]

    Datasheets for datasets.Communications of the ACM, 64(12):86–92, 2021

    Datasheets for Datasets.Commun. ACM64, 12 (nov 2021), 86–92. doi:10.1145/3458723 arXiv:1803.09010 (revised 2021)

    work page doi:10.1145/3458723 2021

  31. [31]

    Guridi, Cristobal Cheyre, Maria Goula, Duarte Santo, Lee Humphreys, Achilleas Souras, and Aishwarya Shankar

    Jose A. Guridi, Cristobal Cheyre, Maria Goula, Duarte Santo, Lee Humphreys, Achilleas Souras, and Aishwarya Shankar. 2025. Image Generative AI to Design Public Spaces: A Reflection of How AI Could Improve Co-Design of Public Parks.Digital Government: Research and Practice6, 1, Article 7 (2025), 14 pages. doi:10.1145/3656588

    work page doi:10.1145/3656588 2025

  32. [32]

    Guridi, Cristobal Cheyre, and Qian Yang

    Jose A. Guridi, Cristobal Cheyre, and Qian Yang. 2025. Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers.Proceedings of the ACM on Human-Computer Interaction9, CSCW2, Article CSCW193 (2025). doi:10.1145/3711091

    work page doi:10.1145/3711091 2025

  33. [33]

    1996.Between Facts and Norms: Contributions to a Discourse Theory of Law and Democracy

    Jürgen Habermas. 1996.Between Facts and Norms: Contributions to a Discourse Theory of Law and Democracy. MIT Press, Cambridge, MA

    1996

  34. [34]

    David Harvey. 2003. The right to the city.International Journal of Urban and Regional Research27, 4 (2003), 939–941. doi:10.1111/j.0309- 1317.2003.00492.x

    work page doi:10.1111/j.0309- 2003

  35. [35]

    1997.Collaborative Planning: Shaping Places in Fragmented Societies

    Patsy Healey. 1997.Collaborative Planning: Shaping Places in Fragmented Societies. Red Globe Press, London. doi:10.1007/978-1-349- 25538-2

    work page doi:10.1007/978-1-349- 1997

  36. [36]

    Tianchen Huang, Xinyue Ye, Tan Yigitcanlar, Boqian Xu, Galen Newman, Bo Zhao, Benjamin Ennemoser, Dayong Wu, Junghwan Kim, and Dongjie Wang. 2026. Artificial Intelligence in Urban Design: A Systematic Review.Cities169 (2026), 106527. doi:10.1016/j.cities. 2025.106527

    work page doi:10.1016/j.cities 2026

  37. [37]

    Ibrahim, John Haworth, and Tao Cheng

    Mohamed R. Ibrahim, John Haworth, and Tao Cheng. 2020. Understanding cities with machine eyes: A review of deep computer vision in urban analytics.Cities96 (2020), 102481. doi:10.1016/j.cities.2019.102481

    work page doi:10.1016/j.cities.2019.102481 2020

  38. [38]

    2004.States of Knowledge: The Co-production of Science and Social Order

    Sheila Jasanoff (Ed.). 2004.States of Knowledge: The Co-production of Science and Social Order. Routledge, London

    2004

  39. [39]

    Kaminski and Jennifer M

    Margot E. Kaminski and Jennifer M. Urban. 2021. The Right to Contest AI.Columbia Law Review121, 7 (2021), 1957–2048. https: //www.jstor.org/stable/27083420

    work page arXiv 2021

  40. [40]

    Yuhao Kang, Jonatan Abraham, Vania Ceccato, Fábio Duarte, Song Gao, Lukas Ljungqvist, Fan Zhang, Per Näsman, and Carlo Ratti

  41. [41]

    doi:10.1016/j.landurbplan.2023.104768

    Assessing differences in safety perceptions using GeoAI and survey across neighbourhoods in Stockholm, Sweden.Landscape and Urban Planning236 (2023), 104768. doi:10.1016/j.landurbplan.2023.104768

    work page doi:10.1016/j.landurbplan.2023.104768 2023

  42. [42]

    Anna Kawakami, Amanda Coston, Hoda Heidari, Kenneth Holstein, and Haiyi Zhu. 2024. Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use.Proceedings of the ACM on Human-Computer Interaction8, CSCW2, Article 450 (2024), 24 pages. doi:10.1145/3686989

    work page doi:10.1145/3686989 2024

  43. [43]

    2014.The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences

    Rob Kitchin. 2014.The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. SAGE Publications Ltd, London

    2014

  44. [44]

    Rob Kitchin. 2014. The real-time city? Big data and smart urbanism.GeoJournal79, 1 (2014), 1–14. doi:10.1007/s10708-013-9516-8

    work page doi:10.1007/s10708-013-9516-8 2014

  45. [45]

    Rob Kitchin. 2016. The ethics of smart cities and urban science.Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences374, 2083 (2016), 20160115. doi:10.1098/rsta.2016.0115

    work page doi:10.1098/rsta.2016.0115 2016

  46. [46]

    Koo and Mae Y

    Terry K. Koo and Mae Y. Li. 2016. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine15, 2 (2016), 155–163. doi:10.1016/j.jcm.2016.02.012

    work page doi:10.1016/j.jcm.2016.02.012 2016

  47. [47]

    1968.Le droit à la ville

    Henri Lefebvre. 1968.Le droit à la ville. Anthropos, Paris

    1968

  48. [48]

    Setha Low. 2020. Social justice as a framework for evaluating public space. InCompanion to Public Space(1 ed.). Routledge, London, UK, 11

    2020

  49. [49]

    Setha M. Low. 2000.On the Plaza: The Politics of Public Space and Culture. University of Texas Press, Austin, TX

    2000

  50. [50]

    Low and Neil Smith (Eds.)

    Setha M. Low and Neil Smith (Eds.). 2006.The Politics of Public Space. Routledge, New York, NY

    2006

  51. [51]

    Shannon Mattern. 2017. A City Is Not a Computer. Places Journal. doi:10.22269/170207 Accessed 2025-12-28

    work page doi:10.22269/170207 2017

  52. [52]

    Vikas Mehta. 2014. Evaluating Public Space.Journal of Urban Design19, 1 (2014), 53–88. doi:10.1080/13574809.2013.854698

    work page doi:10.1080/13574809.2013.854698 2014

  53. [53]

    2016.The Seductions of Quantification: Measuring Human Rights, Gender Violence, and Sex Trafficking

    Sally Engle Merry. 2016.The Seductions of Quantification: Measuring Human Rights, Gender Violence, and Sex Trafficking. University of Chicago Press, Chicago, IL

    2016

  54. [54]

    Don Mitchell. 1995. The End of Public Space? People’s Park, Definitions of the Public, and Democracy.Annals of the Association of American Geographers85, 1 (1995), 108–133. doi:10.1111/j.1467-8306.1995.tb01797.x

    work page doi:10.1111/j.1467-8306.1995.tb01797.x 1995

  55. [55]

    2003.The Right to the City: Social Justice and the Fight for Public Space

    Don Mitchell. 2003.The Right to the City: Social Justice and the Fight for Public Space. Guilford Press, New York, NY

    2003

  56. [56]

    Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model Cards for Model Reporting. InProceedings of the Conference on Fairness, Accountability, and Transparency (FAT* ’19). ACM, New York, NY, USA, 220–229. doi:10.1145/3287560.3287596

    work page doi:10.1145/3287560.3287596 2019

  57. [57]

    2000.The Democratic Paradox

    Chantal Mouffe. 2000.The Democratic Paradox. Verso, London

    2000

  58. [58]

    Rashid Mushkani, Hugo Berard, Allison Cohen, and Shin Koseki. 2025. Position: The Right to AI. InProceedings of the 42nd International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 267), Aarti Singh, Maryam Fazel, Daniel Hsu, Simon Lacoste-Julien, Felix Berkenkamp, Tegan Maharaj, Kiri Wagstaff, and Jerry Zhu (Eds.). PMLR, ...

    2025

  59. [59]

    Rashid Mushkani and Shin Koseki. 2026. Street review: A participatory AI-based framework for assessing streetscape inclusivity.Cities 170 (2026), 106602. doi:10.1016/j.cities.2025.106602

    work page doi:10.1016/j.cities.2025.106602 2026

  60. [60]

    Rashid Mushkani, Shravan Nayak, Hugo Berard, Allison Cohen, Shin Koseki, and Hadrien Bertrand. 2025. LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces. InProceedings of the 42nd International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 267), Aarti Singh, Maryam Fazel, Daniel Hsu, Simon Lacoste-Julien, Fel...

    2025

  61. [61]

    Nikhil Naik, Jade Philipoom, Ramesh Raskar, and César A. Hidalgo. 2014. Streetscore – Predicting the Perceived Safety of One Million Streetscapes. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, Piscataway, NJ, USA, 793–799. doi:10.1109/CVPRW.2014.121

    work page doi:10.1109/cvprw.2014.121 2014

  62. [62]

    2010.Privacy in Context: Technology, Policy, and the Integrity of Social Life

    Helen Nissenbaum. 2010.Privacy in Context: Technology, Policy, and the Integrity of Social Life. Stanford University Press, Stanford, CA. https://www.sup.org/books/law/privacy-context

    2010

  63. [63]

    2018.Algorithms of Oppression: How Search Engines Reinforce Racism

    Safiya Umoja Noble. 2018.Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press, New York, NY

    2018

  64. [64]

    Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022. Training language models to follow instructions with human fee...

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  65. [65]

    2015.The Black Box Society: The Secret Algorithms That Control Money and Information

    Frank Pasquale. 2015.The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press, Cambridge, MA

    2015

  66. [66]

    Ellie Pavlick and Tom Kwiatkowski. 2019. Inherent Disagreements in Human Textual Inferences.Transactions of the Association for Computational Linguistics7 (2019), 677–694. doi:10.1162/tacl_a_00293

    work page doi:10.1162/tacl_a_00293 2019

  67. [67]

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. 2023. SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis. arXiv:2307.01952 [cs.CV] doi:10.48550/arXiv.2307.01952

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2307.01952 2023

  68. [68]

    Theodore M. Porter. 1995.Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton University Press, Princeton, NJ

    1995

  69. [69]

    Marie-Therese Puth, Markus Neuhäuser, and Graeme D. Ruxton. 2015. Effective use of Spearman’s and Kendall’s correlation coefficients for association between two measured traits.Animal Behaviour102 (2015), 77–84. doi:10.1016/j.anbehav.2015.01.010

    work page doi:10.1016/j.anbehav.2015.01.010 2015

  70. [70]

    Direct Preference Optimization: Your Language Model is Secretly a Reward Model

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. 2023. Direct Preference Optimization: Your Language Model is Secretly a Reward Model. arXiv:2305.18290 [cs.LG] doi:10.48550/arXiv.2305.18290

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2305.18290 2023

  71. [71]

    Dillon Reisman, Jason Schultz, Kate Crawford, and Meredith Whittaker. 2018. Algorithmic Impact Assessments Report: A Practical Framework for Public Agency Accountability. AI Now Institute. https://ainowinstitute.org/

    2018

  72. [72]

    Roberts, Volker Bahn, Simone Ciuti, Mark S

    David R. Roberts, Volker Bahn, Simone Ciuti, Mark S. Boyce, Jane Elith, Gurutzeta Guillera-Arroita, Severin Hauenstein, José J. Lahoz-Monfort, Boris Schröder, Wilfried Thuiller, David I. Warton, Brendan A. Wintle, Florian Hartig, and Carsten F. Dormann. 2017. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic struct...

    work page doi:10.1111/ecog.02881 2017

  73. [73]

    Manaswi Saha, Michael Saugstad, Hanuma Teja Maddali, Aileen Zeng, Ryan Holland, Steven Bower, Aditya Dash, Sage Chen, Anthony Li, Kotaro Hara, and Jon Froehlich. 2019. Project Sidewalk: A Web-based Crowdsourcing Tool for Collecting Sidewalk Accessibility Data At Scale. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems(Glasgow,...

    work page doi:10.1145/3290605.3300292 2019

  74. [74]

    James C. Scott. 1998.Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. Yale University Press, New Haven, CT

    1998

  75. [75]

    Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison

    D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison. 2015. Hidden Technical Debt in Machine Learning Systems. InAdvances in Neural Information Processing Systems 28. 2503–2511. https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems

    2015

  76. [76]

    Andrew D. Selbst. 2021. An Institutional View of Algorithmic Impact Assessments.Harvard Journal of Law & Technology35, 1 (2021), 117–191. https://jolt.law.harvard.edu/assets/articlePDFs/v35/Selbst-An-Institutional-View-of-Algorithmic-Impact-Assessments.pdf

    2021

  77. [77]

    Selbst, Danah Boyd, Sorelle A

    Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. Fairness and Abstraction in Sociotechnical Systems. InProceedings of the 2019 Conference on Fairness, Accountability, and Transparency. ACM, New York, NY, USA, 59–68. doi:10.1145/3287560.3287598

    work page doi:10.1145/3287560.3287598 2019

  78. [78]

    Yiming Shao, Ning Ma, Mingxue Chen, Chuni Zhang, and Yuanlong Cui. 2025. Machine Learning in Landscape Architecture: A Compre- hensive Review of Advancements, Applications, and Future Directions.Buildings15, 21 (2025), 3827. doi:10.3390/buildings15213827

    work page doi:10.3390/buildings15213827 2025

  79. [79]

    Taylor Shelton, Matthew Zook, and Alan Wiig. 2015. The ‘actually existing smart city’.Cambridge Journal of Regions, Economy and Society8, 1 (2015), 13–25. doi:10.1093/cjres/rsu026

    work page doi:10.1093/cjres/rsu026 2015

  80. [80]

    Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, and Yejin Choi. 2024. A Roadmap to Pluralistic Alignment. arXiv:2402.05070 [cs.AI] Pluralistic-Alignment Urbanism FAccT ’26, June 25–28, 2026, Montreal, QC, Canada doi:10.48550/arXi...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2402.05070 2024

Showing first 80 references.