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arxiv: 2605.16827 · v1 · pith:LZ6GFYZFnew · submitted 2026-05-16 · 💻 cs.AI

Voices in the Loop: Mapping Participatory AI

Rashid Mushkani This is my paper

Pith reviewed 2026-05-19 21:19 UTC · model grok-4.3

classification 💻 cs.AI
keywords participatory AIAI governanceopen repositorycommunity participationAI ethicsharmonization protocolliving atlas
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The pith

The paper builds a living atlas of participatory AI initiatives that maps their global distribution, typical participation stages, and a governance system for making community input the default in AI systems.

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

This paper describes the construction of an open repository and interactive atlas that collects and harmonizes records of participatory AI projects from existing sources and new audited cases. It specifies a reproducible protocol for discovering, vetting, geocoding, and publishing these records in versioned releases. The work reports patterns showing that documented initiatives concentrate in a small number of countries and that participation occurs most often at problem formulation, evaluation, and governance rather than model development or training. It further demonstrates how the atlas itself functions as a design framework for participatory-by-default AI through linked channels for issues, annotations, schema feedback, and redaction requests. A sympathetic reader would care because the atlas turns scattered examples into a comparable, updatable resource that supports research, policy learning, and ongoing community scrutiny of how AI is shaped.

Core claim

We construct an open atlas of participatory AI initiatives by harmonizing records from prior trustworthy AI corpora with additional audited cases. The atlas reveals corpus-level patterns in geography, participation tiers, lifecycle loci, organizational forms, and documentation gaps while operationalizing a governance framework through versioned releases, record-linked issue channels, schema feedback workflows, and options for redaction or restricted disclosure.

What carries the argument

The participatory AI atlas, a harmonized open repository that enables discovery, vetting, geocoding, provenance tracking, versioned publication, and community-driven updates through issue and annotation channels.

If this is right

  • Documented initiatives remain concentrated in a small number of countries.
  • Participation is coded most often at problem formulation, evaluation, and governance rather than model development or training.
  • The atlas supports comparative research, policy learning, and community scrutiny through a living inventory.
  • Versioned releases and linked feedback channels allow the resource to be updated, contested, and reused over time.

Where Pith is reading between the lines

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

  • The atlas could serve as a base layer for tracking how participation practices evolve when new cases from additional regions are incorporated.
  • Policy efforts might draw on the participation-stage patterns to design requirements that push involvement earlier into technical development phases.
  • Researchers could extend the protocol to include quantitative measures of participation depth or outcome influence beyond the current tiers.

Load-bearing premise

The harmonized records from existing corpora plus additional audited cases form a sufficiently complete and unbiased sample of participatory AI initiatives worldwide, with documentation gaps that do not systematically distort the reported geographic and participation patterns.

What would settle it

Identification of a substantial number of participatory AI initiatives in currently underrepresented countries or regions that significantly shifts the reported geographic concentration or participation stage distributions.

Figures

Figures reproduced from arXiv: 2605.16827 by Rashid Mushkani.

Figure 1
Figure 1. Figure 1: Atlas interface showing the explorer view, map markers, and filter controls for the 131-project release. [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Workflow for constructing and maintaining a living repository and atlas of participatory AI projects. The feedback [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Geographic distribution of participatory AI records (131 projects). The bar chart uses normalized country labels; the [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Completeness of selected atlas fields 5.5 Provenance sources The corpus draws on documentation hosted across 107 distinct provenance domains, indicating a long tail of organizational websites, research pages, and repositories rather than a small set of durable registries. The most common domain suffixes in provenance URLs are .org (46 records), .com (16), .uk (10), .net (8), and .io (7). This distribution … view at source ↗
Figure 5
Figure 5. Figure 5: Projects by mapped region in the March 2026 release. [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of participation tiers across the March 2026 release. [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Most frequently coded AI lifecycle stages, counting projects that list more than one stage. [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
read the original abstract

Participatory approaches to artificial intelligence are increasingly documented across public, civic, and humanitarian settings, but evidence about how participation is organized remains fragmented. This paper reports on the construction of an open repository and interactive atlas of participatory AI initiatives, using records harmonized from Maga~na and Shilton's Trustworthy AI corpus, and additional audited cases from research and practice. We contribute three elements. First, we specify a reproducible protocol for discovery, vetting, harmonization, geocoding, provenance tracking, and release-based publication of participatory AI records. Second, we report corpus-level patterns in geography, participation tiers, lifecycle loci, organizational form, verification status, and remaining documentation gaps. Documented initiatives remain concentrated in a small number of countries, while participation is most often coded at problem formulation, evaluation, and governance rather than model development or training. Third, we show how the atlas operationalizes a design and governance framework for participatory-by-default AI infrastructures through versioned releases, record-linked issue and annotation channels, schema feedback workflows, and redaction or restricted-disclosure requests. The atlas is intended to support comparative research, policy learning, and community scrutiny through a living inventory that can be updated, contested, and reused.

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

2 major / 2 minor

Summary. The paper describes the construction of an open repository and interactive atlas of participatory AI initiatives. It harmonizes records from the Magaña and Shilton Trustworthy AI corpus with additional audited cases. The authors specify a reproducible protocol for discovery, vetting, harmonization, geocoding, provenance tracking, and release-based publication. They report corpus-level patterns regarding geography, participation tiers, lifecycle loci, organizational form, verification status, and documentation gaps. Finally, they illustrate how the atlas operationalizes a design and governance framework for participatory-by-default AI infrastructures via versioned releases, record-linked channels, schema feedback, and redaction requests.

Significance. If the reported patterns hold, this work provides a significant contribution by creating a living, open inventory that can facilitate comparative research, policy learning, and community scrutiny in participatory AI. The emphasis on a reproducible protocol, provenance tracking, versioned releases, and mechanisms for updates, contestation, and feedback represents a concrete strength that supports more inclusive AI design and governance. The paper explicitly credits the value of open data practices and community involvement in building such resources.

major comments (2)
  1. [Abstract (protocol contribution)] The reproducible protocol for discovery, vetting, harmonization, geocoding, and provenance tracking is described at a high level in the abstract but lacks explicit enumeration of search sources, inclusion/exclusion rules, or quantitative checks for language/visibility bias. This directly affects the reliability of the reported geographic concentrations and participation patterns.
  2. [Abstract (corpus-level patterns contribution)] The corpus-level patterns in geography, participation tiers, lifecycle loci, and documentation gaps are reported without quantitative details, error estimates, or verification steps. The central claim that these patterns reflect actual distributions rather than visibility biases rests on the unverified assumption that the harmonized Magaña-Shilton corpus plus audited cases form a sufficiently complete and unbiased sample.
minor comments (2)
  1. The abstract contains 'Maga~na' which appears to be a typographical rendering of 'Magaña'; verify and correct spelling consistency across the manuscript and references.
  2. [Atlas operationalization section] The description of the interactive atlas features and record-linked issue channels could be expanded with a brief example or figure to clarify how they operationalize the governance framework.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive and detailed comments, which help clarify the presentation of our protocol and findings. We address each major comment below, indicating planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract (protocol contribution)] The reproducible protocol for discovery, vetting, harmonization, geocoding, and provenance tracking is described at a high level in the abstract but lacks explicit enumeration of search sources, inclusion/exclusion rules, or quantitative checks for language/visibility bias. This directly affects the reliability of the reported geographic concentrations and participation patterns.

    Authors: We agree that the abstract presents the protocol at a summary level for brevity. The full manuscript details the protocol in the Methods section, specifying sources (starting from the Magaña-Shilton corpus with additional targeted searches), inclusion criteria focused on documented participatory elements, and steps for harmonization, geocoding, and provenance. To directly address the concern, we will revise the abstract to enumerate key search sources and core inclusion/exclusion rules in one additional sentence, and add a dedicated paragraph on bias considerations with available quantitative checks such as language distribution across records. revision: yes

  2. Referee: [Abstract (corpus-level patterns contribution)] The corpus-level patterns in geography, participation tiers, lifecycle loci, and documentation gaps are reported without quantitative details, error estimates, or verification steps. The central claim that these patterns reflect actual distributions rather than visibility biases rests on the unverified assumption that the harmonized Magaña-Shilton corpus plus audited cases form a sufficiently complete and unbiased sample.

    Authors: The manuscript reports specific patterns with supporting counts (e.g., geographic concentration and lifecycle stage distributions) drawn from the harmonized corpus of audited cases. We acknowledge that formal error estimates are not provided and that visibility bias cannot be fully ruled out. In revision we will expand the Results section with explicit percentages, counts per category, and a clearer description of the auditing and verification process. We will also revise the abstract and discussion to frame the patterns as observations from the current corpus rather than claims of unbiased global distributions, while noting the living nature of the atlas as a mechanism for future completeness improvements. revision: partial

standing simulated objections not resolved
  • A fully quantitative verification that the corpus is free of visibility bias is not feasible given the decentralized and often undocumented nature of participatory AI initiatives; the auditing process described provides the strongest practical check available.

Circularity Check

0 steps flagged

No circularity: descriptive mapping from external corpus with new protocol

full rationale

The paper specifies a protocol for harmonizing records from an external corpus (Magaña and Shilton) plus audited cases, then reports observed patterns in geography and participation without any mathematical derivations, fitted parameters, or predictions. No equations, self-citations, or ansatzes reduce claims to inputs by construction; the atlas and framework are presented as operational outputs of the described processes rather than tautological restatements. The work is self-contained as a reproducible inventory construction and descriptive analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No free parameters or invented entities are introduced. The work rests on the domain assumption that participatory AI cases can be reliably discovered, vetted, and harmonized from public and research sources without major unaddressed selection biases.

axioms (1)
  • domain assumption Participatory AI initiatives can be identified, categorized by participation tier and lifecycle stage, and geocoded from existing corpora and additional audited sources with acceptable documentation gaps.
    Invoked to justify corpus construction and pattern reporting in the abstract.

pith-pipeline@v0.9.0 · 5735 in / 1393 out tokens · 59985 ms · 2026-05-19T21:19:23.393475+00:00 · methodology

discussion (0)

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Works this paper leans on

47 extracted references · 47 canonical work pages

  1. [1]

    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

  2. [2]

    Bender and Batya Friedman

    Emily M. Bender and Batya Friedman. 2018. Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science.Transactions of the Association for Computational Linguistics6 (2018), 587–604. doi:10.1162/tacl_a_00041

    work page doi:10.1162/tacl_a_00041 2018

  3. [3]

    Aleks Berditchevskaia, Eirini Malliaraki, and Kathy Peach. 2021. Participatory AI for Humanitarian Innovation. Nesta Briefing Paper. https://media.nesta.org.uk/documents/Nesta_Participatory_AI_for_humanitarian_innovation_Final.pdf Accessed 2026-01-14

    work page 2021

  4. [4]

    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, Mechanisms, and Optimization(Arlington, VA, USA)(EAAMO ’22). Association for Com...

    work page doi:10.1145/3551624.3555290 2022

  5. [5]

    Cooper, Janis Dickinson, Steve Kelling, Tina Phillips, Kenneth V

    Rick Bonney, Caren B. Cooper, Janis Dickinson, Steve Kelling, Tina Phillips, Kenneth V. Rosenberg, and Jennifer Shirk. 2009. Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy.BioScience59, 11 (2009), 977–984. doi:10.1525/bio. 2009.59.11.9

    work page doi:10.1525/bio 2009

  6. [6]

    Bowker and Susan Leigh Star

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

    work page 1999

  7. [7]

    Figueroa-Rodríguez, Jarita Holbrook, Raymond Lovett, Simeon Materechera, Mark Parsons, Kay Raseroka, Desi Rodriguez-Lonebear, Robyn Rowe, Rodrigo Sara, Jennifer D

    Stephanie Russo Carroll, Ibrahim Garba, Oscar L. Figueroa-Rodríguez, Jarita Holbrook, Raymond Lovett, Simeon Materechera, Mark Parsons, Kay Raseroka, Desi Rodriguez-Lonebear, Robyn Rowe, Rodrigo Sara, Jennifer D. Walker, Jane Anderson, and Maui Hudson

    work page

  8. [8]

    Figueroa-Rodr´ ıguez, Jarita Holbrook, Raymond Lovett, Simeon Materechera, Mark Parsons, Kay Raseroka, Desi Rodriguez-Lonebear, Robyn Rowe, Rodrigo Sara, Jennifer D

    The CARE Principles for Indigenous Data Governance.Data Science Journal19, 1 (2020), 43. doi:10.5334/dsj-2020-043

    work page doi:10.5334/dsj-2020-043 2020

  9. [9]

    City of Amsterdam. 2022. Grip on Algorithms: The Algorithm Lifecycle Approach. Guideline. https://assets.amsterdam.nl/publish/ pages/1053010/playbook_algorithms.pdf Accessed 2026-01-14

    work page arXiv 2022

  10. [10]

    City of Helsinki. 2020. AI Register. Municipal website. https://ai.hel.fi/en/ai-register/ Accessed 2026-01-14

    work page 2020

  11. [11]

    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. MIT Press, Cambridge, MA. FAccT ’26, June 25–28, 2026, Montreal, QC, Canada Mushkani

    work page 2020

  12. [12]

    Bradley Cousins and Elizabeth Whitmore

    J. Bradley Cousins and Elizabeth Whitmore. 1998. Framing Participatory Evaluation.New Directions for Evaluation1998, 80 (1998), 5–23. doi:10.1002/ev.1114

    work page doi:10.1002/ev.1114 1998

  13. [13]

    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 (Boston, MA, USA)(EAAMO ’23). Association for Computing Machinery, New York, NY, USA, A...

    work page doi:10.1145/3617694.3623261 2023

  14. [14]

    Digital Forum Lab and Eurocities. 2022. Algorithmic Transparency Standard. https://standard.algorithmictransparency.org/ Accessed 2026-02-16

    work page 2022

  15. [15]

    Catherine D’Ignazio and Lauren F. Klein. 2020.Data Feminism. MIT Press, Cambridge, MA

    work page 2020

  16. [16]

    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

    work page 2018

  17. [17]

    Batya Friedman and David G. Hendry. 2019.Value Sensitive Design: Shaping Technology with Moral Imagination. MIT Press, Cambridge, MA

    work page 2019

  18. [18]

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

    work page doi:10.1111/j 2006

  19. [19]

    Archon Fung and Mark E. Warren. 2011. The Participedia Project: An Introduction.International Public Management Journal14, 3 (2011), 341–362. doi:10.1080/10967494.2011.618309

    work page doi:10.1080/10967494.2011.618309 2011

  20. [20]

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

    work page

  21. [21]

    URL https://cacm.acm.org/research/ datasheets-for-datasets/

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

    work page doi:10.1145/3458723 2021

  22. [22]

    2019.Chasing Innovation: Making Entrepreneurial Citizens in Modern India

    Lilly Irani. 2019.Chasing Innovation: Making Entrepreneurial Citizens in Modern India. Princeton University Press, Princeton, NJ

    work page 2019

  23. [23]

    Peter-Lucas Jones. 2025. Kaitiaki: Closing the Door on Open Indigenous Data.International Journal on Digital Libraries26, 1 (2025), 1. doi:10.1007/s00799-025-00410-2

    work page doi:10.1007/s00799-025-00410-2 2025

  24. [24]

    Anna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari, and Kenneth Holstein. 2024. The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder, Early-stage Deliberations Around Public Sector AI Proposals. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA...

    work page arXiv 2024

  25. [25]

    2007.Guidelines for Performing Systematic Literature Reviews in Software Engineering

    Barbara Kitchenham and Stuart Charters. 2007.Guidelines for Performing Systematic Literature Reviews in Software Engineering. Technical Report EBSE-2007-01. Keele University and Durham University Joint Report

    work page 2007

  26. [26]

    Rob Kitchin. 2017. Thinking Critically About and Researching Algorithms.Information, Communication & Society20, 1 (2017), 14–29

    work page 2017

  27. [27]

    2016.Indigenous Data Sovereignty: Toward an Agenda

    Tahu Kukutai and John Taylor (Eds.). 2016.Indigenous Data Sovereignty: Toward an Agenda. ANU Press, Canberra

    work page 2016

  28. [28]

    Julien Landry and Bettina von Lieres. 2022. Strengthening Democracy Through Knowledge Mobilization: Participedia – A Citizen-Led Global Platform for Transformative and Democratic Learning.Journal of Transformative Education20, 3 (2022), 206–225. doi:10.1177/ 15413446221103191

    work page 2022

  29. [29]

    Gianna Leoni, Lee Steven, Keith T¯ureiti, Keoni Mahelona, Peter-Lucas Jones, and Suzanne Duncan. 2024. Solving Failure Modes in the Creation of Trustworthy Language Technologies. InProceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024, Maite Melero, Sakriani Sakti, and Claudia Soria (Eds.). ELR...

    work page 2024

  30. [30]

    Maria Isabel Magaña and Katie Shilton. 2025. Frameworks, Methods and Shared Tasks: Connecting Participatory AI to Trustworthy AI Through a Systematic Review of Global Projects. InProceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York, NY, USA, 2166–2179. doi:10.1145/3715275.3732148

    work page doi:10.1145/3715275.3732148 2025

  31. [31]

    2015.Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response

    Patrick Meier. 2015.Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response. CRC Press, Boca Raton, FL

    work page 2015

  32. [32]

    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. Association for Computing Machinery, New York, NY, USA, 220–229. doi:10.1145/3287560.3287596

    work page doi:10.1145/3287560.3287596 2019

  33. [33]

    Rashid Mushkani. 2026. Measuring What Matters: The AI Pluralism Index. doi:10.48550/arXiv.2510.08193 Proceedings of the International Association for Safe & Ethical AI (IASEAI), 2026

    work page doi:10.48550/arxiv.2510.08193 2026

  34. [34]

    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, ...

    work page 2025

  35. [35]

    Tina Nabatchi. 2012. Putting the “Public” Back in Public Values Research: Designing Participation to Identify and Respond to Values. Public Administration Review72, 5 (2012), 699–708. doi:10.1111/j.1540-6210.2012.02544.x

    work page doi:10.1111/j.1540-6210.2012.02544.x 2012

  36. [36]

    New York City Automated Decision Systems Task Force. 2019. Automated Decision Systems Task Force Report. Report. https: //www.nyc.gov/site/adstaskforce/index.page Accessed 2026-01-14

    work page 2019

  37. [37]

    Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , location =

    Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. 2020. Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Voices in the Loop FAccT ’26, June 25–28, 2026, Montreal, QC, Canada Auditing. InProceedings of the 2...

    work page doi:10.1145/3351095.3372873 2020

  38. [38]

    Dillon Reisman, Jason Schultz, Kate Crawford, and Meredith Whittaker. 2018. Algorithmic Impact Assessments: A Practical Framework for Public Agency Accountability. AI Now Institute Report. https://ainowinstitute.org/wp-content/uploads/2023/04/aiareport2018.pdf Accessed 2026-01-14

    work page 2018

  39. [39]

    Toni Robertson and Jesper Simonsen. 2012. Participatory Design: An Introduction. InRoutledge International Handbook of Participatory Design, Jesper Simonsen and Toni Robertson (Eds.). Routledge, New York, NY, 1–17

    work page 2012

  40. [40]

    1993.Participatory Design: Principles and Practices

    Douglas Schuler and Aki Namioka (Eds.). 1993.Participatory Design: Principles and Practices. Lawrence Erlbaum Associates, Hillsdale, NJ

    work page 1993

  41. [41]

    Model Cards for Model Reporting,

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

    work page doi:10.1145/3287560.3287598 2019

  42. [42]

    2012.Routledge International Handbook of Participatory Design

    Jesper Simonsen and Toni Robertson (Eds.). 2012.Routledge International Handbook of Participatory Design. Routledge, New York, NY

    work page 2012

  43. [43]

    Mona Sloane, Emanuel Moss, Olaitan Awomolo, and Laura Forlano. 2022. Participation Is not a Design Fix for Machine Learning. In Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization(Arlington, VA, USA)(EAAMO ’22). Association for Computing Machinery, New York, NY, USA, Article 1. doi:10.1145/3551624.3555285

    work page doi:10.1145/3551624.3555285 2022

  44. [44]

    Susan Leigh Star. 1999. The Ethnography of Infrastructure.American Behavioral Scientist43, 3 (1999), 377–391

    work page 1999

  45. [45]

    Susan Leigh Star and Karen Ruhleder. 1996. Steps Toward an Ecology of Infrastructure: Design and Access for Large Information Spaces. Information Systems Research7, 1 (1996), 111–134

    work page 1996

  46. [46]

    UK Central Digital and Data Office. 2023. Algorithmic Transparency Recording Standard. GOV.UK guidance. https://www.gov.uk/ government/collections/algorithmic-transparency-recording-standard-hub Accessed 2026-01-14

    work page 2023

  47. [47]

    Maranke Wieringa. 2020. What to Account for When Accounting for Algorithms: A Systematic Literature Review on Algorithmic Accountability. InProceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York, NY, USA, 1–18. FAccT ’26, June 25–28, 2026, Montreal, QC, Canada Mushkani A Supplementar...

    work page 2020