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Cited by 256Ethics and Social Impacts of AIInformation Systems Theories and ImplementationInnovative Human-Technology Interaction

The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice

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Delgado, Fernando, Yang, Stephen, Madaio, Michael, Yang, Qian·2023·Equity and Access in Algorithms, Mechanisms, and Optimization
DOI 10.1145/3617694.3623261
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Abstract

Despite the growing consensus that stakeholders affected by AI systems should participate in their design, enormous variation and implicit disagreements exist among current approaches. For researchers and practitioners who are interested in taking a participatory approach to AI design and development, it remains challenging to assess the extent to which any participatory approach grants substantive agency to stakeholders. This article thus aims to ground what we dub the “participatory turn” in AI design by synthesizing existing theoretical literature on participation and through empirical investigation and critique of its current practices. Specifically, we derive a conceptual framework through synthesis of literature across technology design, political theory, and the social sciences that researchers and practitioners can leverage to evaluate approaches to participation in AI design. Additionally, we articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners. We use these empirical findings to understand the current state of participatory practice and subsequently provide guidance to better align participatory goals and methods in a way that accounts for practical constraints.

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  • Approaches that enable communities to define and curate datasets remain underexplored.

    “Despite calls for more inclusive data-centric AI (e.g., [11, 31, 53, 96, 107]), approaches that enable communities to define and curate datasets remain underexplored.”

    From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [31]

    The Introduction describes growing calls for stakeholder participation in AI design, including data generation and annotation. The Discussion (§4) finds that only a handful of surveyed projects let stakeholders drive or own any part of design. This supports the broader concern about limited community agency, but the paper does not specifically establish that approaches for communities to define and curate datasets remain underexplored. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • The most dominant approach to gathering human annotations is to 'crowd-source' either anonymous people or community members as 'data workers', who are given simple, tokenistic tasks with little or no context to the data.

    “The most dominant approach to gathering human annotations is to 'crowd-source' [7, 53, 65] either anonymous people or community members as 'data workers', who are given simple, tokenistic tasks 'that even inexperienced annotators can finish in an instance' [31, 85], with little or no context to the data.”

    From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [31]

    The Introduction describes many practitioners involving stakeholders as “data labor,” including in data labeling or annotation. It does not establish that crowdsourcing is the most dominant way to gather annotations, or describe annotators as anonymous people or community members given little context. The quotation about simple tasks is attached to different citations in the citing sentence. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Researchers have previously problematized anonymous crowdsourced data annotation approaches as being unspecific about who's worldviews and beliefs become manifest in the data.

    “Researchers have previously problematized anonymous crowdsourced data annotation approaches [31, 53, 65] as being unspecific about who's worldviews and beliefs become manifest in the data [11, 17, 51, 78, 90, 91].”

    From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [31]

    Section 4.3.1 (“Mistaken identity”; page unavailable) questions whether people selected as stand-ins can represent others’ preferences and values, and warns that selection bias can exclude marginalized community members. The Introduction mentions data labeling and annotation, but our copy does not make this critique specifically about anonymous crowdsourced annotation or say that such approaches leave annotators’ worldviews unidentified. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

  • Participatory design allows Indigenous communities to retain agency over the technology they use and the computational practices they entail, ensuring that it serves their priorities rather than imposing external values.

    “This approach allows Indigenous communities to retain agency over the technology they use and the computational practices they entail, ensuring that it serves their priorities rather than imposing external values [15].”

    From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [15]

    Section 2.4.2 says stakeholder empowerment depends on having decision-making power over both a system’s design and the process used to develop it. But the paper does not establish this specifically for Indigenous communities, and its Discussion says most participatory AI projects afford stakeholders little design agency. It therefore supports the aim of retaining agency, not the claim that this approach ensures it. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Participatory design is central to the realisation of RAI in Indigenous contexts, prioritising community participation throughout the AI lifecycle.

    “Participatory design is central to the realisation of RAI in these contexts, which prioritises community participation throughout the AI lifecycle, ensuring that community members actively shape AI systems to reflect their needs, values, and knowledge systems [10, 15].”

    From Designing Responsible AI with Indigenous Communities: Towards Participatory AI with ovaHimba and San People in Namibia (2026) · cites this paper as [15]

    The Introduction (no page given) describes calls for affected communities to shape AI design so systems reflect their values and needs. But the paper does not establish this specifically for Indigenous contexts or their knowledge systems. Section 3.2.1 (no page given) also reports that community stakeholders participated throughout the project lifecycle in only 3 of 80 projects, rather than describing lifecycle-long participation as current practice. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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References 159 · Cited by 256

Research works printed but not linked: 189

  1. Rediet Abebe and Kira Goldner. 2018. Mechanism design for social good. AI Matters 4, 3 (2018), 27-34.
  2. Julia Abelson, Pierre-Gerlier Forest, John Eyles, Patricia Smith, Elisabeth Martin, and Francois-Pierre Gauvin. 2003. Deliberations about deliberative methods: issues in the design and evaluation of public participation processes. Social science & medicine 57, 2 (2003), 239-251.
  3. Oscar Alvarado, Elias Storms, David Geerts, and Katrien Verbert. 2020. Fore- grounding Algorithms: Preparing Users for Co-design with Sensitizing Activities. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society. 1-7.
  4. Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, et al. 2019. Guidelines for human-AI interaction. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1-13.
  5. Mike Ananny and Kate Crawford. 2018. Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. new media & society 20, 3 (2018), 973-989.
  6. Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua R. Gubler, Christopher Rytting, and David Wingate. 2023. Out of One, Many: Using Language Models to Simulate Human Samples. Political Analysis 31, 3 (2023), 337-351. https: //doi.org/10.1017/pan.2023.2 · DOI · Paper page
  7. Kenneth J Arrow. 2012. Social choice and individual values. Yale university press.
  8. Shiri Azenkot, Catherine Feng, and Maya Cakmak. 2016. Enabling building service robots to guide blind people a participatory design approach. In 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 3-10.
  9. Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862 (2022). · arXiv
  10. Ellen Balka. 2010. Broadening discussion about participatory design. Scandina- vian Journal of Information Systems 22, 1 (2010), 77-84.
  11. Julia Barnett and Nicholas Diakopoulos. 2022. Crowdsourcing impacts: exploring the utility of crowds for anticipating societal impacts of algorithmic decision making. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society. 56-67.
  12. Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, and Dominik Moritz. 2022. Symphony: Compos- ing Interactive Interfaces for Machine Learning. In CHI Conference on Human Factors in Computing Systems. 1-14.
  13. Eric PS Baumer. 2017. Toward human-centered algorithm design. Big Data & Society 4, 2 (2017), 2053951717718854.
  14. Jesse Josua Benjamin, Christoph Kinkeldey, Claudia Müller-Birn, Tim Korjakow, and Eva-Maria Herbst. [n. d.]. Explanation Strategies as an Empirical-Analytical Lens for Socio-Technical Contextualization of Machine Learning Interpretability. 6 ([n. d.]), 1-25. Issue GROUP. https://doi.org/10.1145/3492858 · DOI
  15. Cynthia L Bennett and Daniela K Rosner. 2019. The Promise of Empathy: Design, Disability, and Knowing the" Other". In Proceedings of the 2019 CHI conference on human factors in computing systems. 1-13.
  16. Rahul Bhargava, Anna Chung, Neil S Gaikwad, Alexis Hope, Dennis Jen, Jasmin Rubinovitz, Belén Saldías-Fuentes, and Ethan Zuckerman. 2019. Gobo: A system for exploring user control of invisible algorithms in social media. In Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing. 151-155.
  17. 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. In Equity and Access in Algorithms, Mechanisms, and Optimization (Arlington, VA, USA) (EAAMO '22). Association for Computing Machinery, New York, NY, USA, Article 6, 8 pages. https://doi.org/10.1145/3551624.3555290 · DOI · Paper page
  18. Gro Bjerknes and Tone Bratteteig. 1995. User participation and democracy: A discussion of Scandinavian research on system development. Scandinavian Journal of information systems 7, 1 (1995), 1.
  19. Emily Black, Joshua Williams, Michael A Madaio, and Priya L Donti. 2020. A call for universities to develop requirements for community engagement in AI research. In The Fair and Responsible AI Workshop at the 2020 CHI Conference on Human Factors in Computing Systems.
  20. Design Jeanette Blomberg and Helena Karasti. 2012. Ethnography: Positioning ethnography within participatory design. In Routledge international handbook of participatory design. Routledge, 106-136.
  21. Susanne Bødker and Morten Kyng. 2018. Participatory Design That Mat- ters-Facing the Big Issues. ACM Trans. Comput.-Hum. Interact. 25, 1, Article 4 (feb 2018), 31 pages. https://doi.org/10.1145/3152421 · DOI
  22. Elizabeth Bondi, Lily Xu, Diana Acosta-Navas, and Jackson A. Killian. 2021. Envisioning Communities: A Participatory Approach Towards AI for Social Good. Association for Computing Machinery, New York, NY, USA, 425-436. https: //doi.org/10.1145/3461702.3462612 · DOI · Paper page
  23. Alan Borning, Batya Friedman, and P Kahn. 2004. Designing for human values in an urban simulation system: Value sensitive design and participatory design. In Proceedings From the Eighth Biennial Participatory Design Conference. Citeseer.
  24. Nina Bozic Yams and Álvaro Aranda Muñoz. 2021. Poetics of Future Work: Blending Speculative Design with Artistic Methodology. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. 1-8.
  25. Tone Bratteteig and Guri Verne. 2018. Does AI Make PD Obsolete? Explor- ing Challenges from Artificial Intelligence to Participatory Design. In Proceed- ings of the 15th Participatory Design Conference: Short Papers, Situated Actions, Workshops and Tutorial -Volume 2 (Hasselt and Genk, Belgium) (PDC '18). As- sociation for Computing Machinery, New York, NY, USA, Article 8, 5 pages. https://doi.org/10.1145/3210604.3210646 · DOI
  26. Tone Bratteteig and Ina Wagner. 2012. Disentangling power and decision- making in participatory design. In Proceedings of the 12th Participatory Design Conference: Research Papers-Volume 1. 41-50.
  27. Tone Bratteteig and Ina Wagner. 2016. Unpacking the Notion of Participation in Participatory Design. Comput. Supported Coop. Work 25, 6 (dec 2016), 425-475. https://doi.org/10.1007/s10606-016-9259-4 · DOI
  28. Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77-101.
  29. Anna Brown, Alexandra Chouldechova, Emily Putnam-Hornstein, Andrew To- bin, and Rhema Vaithianathan. 2019. Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algo- rithmic Decision-Making in Child Welfare Services. Association for Computing Machinery, New York, NY, USA, 1-12. https://doi-org.proxy.library.cornell. edu/10.1145/3290605.3300271 · DOI · Paper page
  30. Jenna Burrell. 2016. How the machine 'thinks': Understanding opacity in machine learning algorithms. Big data & society 3, 1 (2016), 2053951715622512.
  31. Dan Calacci and Alex Pentland. [n. d.]. Bargaining with the Black-Box: Design- ing and Deploying Worker-Centric Tools to Audit Algorithmic Management. 6 ([n. d.]), 1-24. Issue CSCW2. https://doi.org/10.1145/3570601 · DOI
  32. Heloisa Candello, Mauro Pichiliani, and Claudio Pinhanez. 2021. Making Conversational Robots Learn from Children in a Collaborative Museum Exhi- bition. Association for Computing Machinery, New York, NY, USA, 208-212. https://doi-org.proxy.library.cornell.edu/10.1145/3462204.3482888 · DOI
  33. Heloisa Candello, Mauro Pichiliani, Claudio Pinhanez, Sara Vidon, and Mairieli Wessel. 2020. Co-designing a conversational interactive exhibit for children. In Proceedings of the 2020 ACM Interaction Design and Children Conference: Extended Abstracts. 326-331.
  34. Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al. 2023. Open Problems and Fundamental Limitations of Reinforce- ment Learning from Human Feedback. arXiv preprint arXiv:2307.15217 (2023). · arXiv
  35. Kyla Chasalow and Karen Levy. 2021. Representativeness in Statistics, Politics, and Machine Learning. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 77-89.
  36. Zhilong Chen, Jinghua Piao, Xiaochong Lan, Hancheng Cao, Chen Gao, Zhicong Lu, and Yong Li. [n. d.]. Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design. 6 ([n. d.]), 533:1-533:32. Issue CSCW2. https://doi.org/10.1145/3555646 · DOI
  37. Hao-Fei Cheng, Logan Stapleton, Ruiqi Wang, Paige Bullock, Alexandra Choulde- chova, Zhiwei Steven Steven Wu, and Haiyi Zhu. 2021. Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1-17.
  38. Parmit K Chilana, Andrew J Ko, and Jacob Wobbrock. 2015. From user-centered to adoption-centered design: a case study of an HCI research innovation be- coming a product. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 1749-1758.
  39. Chhaya Chouhan, Christy M LaPerriere, Zaina Aljallad, Jess Kropczynski, Heather Lipford, and Pamela J Wisniewski. 2019. Co-designing for commu- nity oversight: Helping people make privacy and security decisions together. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1-31.
  40. A Feder Cooper, Emanuel Moss, Benjamin Laufer, and Helen Nissenbaum. 2022. Accountability in an algorithmic society: relationality, responsibility, and ro- bustness in machine learning. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 864-876.
  41. Sasha Costanza-Chock. 2020. Design justice: Community-led practices to build the worlds we need. The MIT Press.
  42. Andy Crabtree. 1998. Ethnography in participatory design. In Proceedings of the 1998 Participatory design Conference. Seattle, WA, 93-105.
  43. Kenneth Cukier and Viktor Mayer-Schoenberger. 2014. The rise of big data: How it's changing the way we think about the world. Princeton University Press.
  44. Andy Dearden, Ann Light, Theodore Zamenopoulos, Paula Graham, Emma Plouviez, and Sophia de Sousa. 2014. Scaling up co-design: research projects as design things. In Proceedings of the 13th Participatory Design Conference: Short Papers, Industry Cases, Workshop Descriptions, Doctoral Consortium papers, and Keynote abstracts-Volume 2. 67-70.
  45. Emily Denton, Alex Hanna, Razvan Amironesei, Andrew Smart, Hilary Nicole, and Morgan Klaus Scheuerman. 2020. Bringing the People Back In: Contesting Benchmark Machine Learning Datasets. (2020). arXiv:2007.07399 [cs.CY] · arXiv
  46. John Dewey. 1954. {1927} The Public and Its Problems. Athens, OH: Ohio University Press.
  47. Jesse Dodge, Maarten Sap, Ana Marasović, William Agnew, Gabriel Ilharco, Dirk Groeneveld, Margaret Mitchell, and Matt Gardner. 2021. Documenting large webtext corpora: A case study on the colossal clean crawled corpus. arXiv preprint arXiv:2104.08758 (2021). · arXiv
  48. Pedro Domingos. 2012. A few useful things to know about machine learning. Commun. ACM 55, 10 (2012), 78-87.
  49. Paul Dourish, Christopher Lawrence, Tuck Wah Leong, and Greg Wadley. 2020. On Being Iterated: The Affective Demands of Design Participation. Association for Computing Machinery, New York, NY, USA, 1-11. https://doi.org/10.1145/ 3313831.3376545 · DOI
  50. Carlos Duarte, Luis Carriço, David Costa, Daniel Costa, André Falcão, and Luís Tavares. 2014. Welcoming gesture recognition into autism therapy. In CHI'14 Extended Abstracts on Human Factors in Computing Systems. 1267-1272.
  51. Upol Ehsan and Mark O Riedl. 2020. Human-centered explainable ai: Towards a reflective sociotechnical approach. In HCI International 2020-Late Breaking Papers: Multimodality and Intelligence: 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19-24, 2020, Proceedings 22. Springer, 449-466.
  52. John Elliot. 1991. Action research for educational change. McGraw-Hill Education (UK).
  53. Jenny Fan and Amy X Zhang. 2020. Digital juries: A civics-oriented approach to platform governance. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1-14.
  54. Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K Patro, Manish Raghavan, Ana-Andreea Stoica, and Stratis Tsirtsis. 2021. Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 489-503.
  55. James S Fishkin and Robert C Luskin. 2005. Experimenting with a democratic ideal: Deliberative polling and public opinion. Acta politica 40, 3 (2005), 284-298.
  56. James S Fishkin and Jane Mansbridge. 2017. The prospects and limits of deliber- ative democracy: Introduction. Daedalus 146, 3 (2017), 6-13.
  57. Jodi Forlizzi. 2018. Moving beyond user-centered design. interactions 25, 5 (2018), 22-23.
  58. Jodi Forlizzi and John Zimmerman. 2013. Promoting service design as a core practice in interaction design. In Proceedings of the 5th International Congress of International Association of Societies of Design Research-IASDR, Vol. 13.
  59. Christopher Frauenberger, Judith Good, G. Fitzpatrick, and Ole Iversen. 2015. In pursuit of rigour and accountability in participatory design. International Journal of Human-Computer Studies 74 (2015), 93 -106.
  60. Christopher Frauenberger, Judith Good, Wendy Keay-Bright, and Helen Pain. 2012. Interpreting input from children: a designerly approach. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 2377-2386. http://doi.org/10.1145/2207676.2208399 · DOI
  61. Batya Friedman. 1996. Value-sensitive design. interactions 3, 6 (1996), 16-23.
  62. Archon Fung. 2006. Varieties of participation in complex governance. Public administration review 66 (2006), 66-75.
  63. Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al. 2022. Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858 (2022). · arXiv
  64. Theodoros Georgiou, Lynne Baillie, Martin K Ross, and Frank Broz. 2020. Apply- ing the Participatory Design Workshop Method to Explore how Socially Assis- tive Robots Could Assist Stroke Survivors. In Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction. 203-205.
  65. Tarleton Gillespie. 2020. Content moderation, AI, and the question of scale. Big Data & Society 7, 2 (2020), 2053951720943234.
  66. Janne Gleerup, Lars Hulgaard, and Simon Teasdale. 2019. Action research and participatory democracy in social enterprise. Social Enterprise Journal (2019).
  67. Mitchell L Gordon, Michelle S Lam, Joon Sung Park, Kayur Patel, Jeff Hancock, Tatsunori Hashimoto, and Michael S Bernstein. 2022. Jury learning: Integrating dissenting voices into machine learning models. In CHI Conference on Human Factors in Computing Systems. 1-19.
  68. Judith Gregory. 2003. Scandinavian approaches to participatory design. Inter- national Journal of Engineering Education 19, 1 (2003), 62-74.
  69. Erik Grönvall and Stefan Lundberg. 2019. PYCIPEDIA: Supporting Local and Remote Collaboration between Social Workers.. In CSCW Companion. 195-200.
  70. Lara Groves, Aidan Peppin, Andrew Strait, and Jenny Brennan. 2023. Going public: the role of public participation approaches in commercial AI labs. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Trans- parency. 1162-1173.
  71. Aaron Halfaker and R Stuart Geiger. 2020. Ores: Lowering barriers with participa- tory machine learning in wikipedia. Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020), 1-37.
  72. Perttu Hämäläinen, Mikke Tavast, and Anton Kunnari. 2023. Evaluating large language models in generating synthetic hci research data: a case study. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. 1-19.
  73. Alex Hanna and Tina M Park. 2020. Against Scale: Provocations and Resistances to Scale Thinking. arXiv preprint arXiv:2010.08850 (2020). · arXiv
  74. Christina Harrington, Sheena Erete, and Anne Marie Piper. 2019. Deconstructing community-based collaborative design: Towards more equitable participatory design engagements. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1-25.
  75. Gillian R Hayes. 2014. Knowing by doing: action research as an approach to HCI. In Ways of Knowing in HCI. Springer, 49-68.
  76. Sara Heitlinger, Lara Houston, Alex Taylor, and Ruth Catlow. 2021. Algorithmic Food Justice: Co-Designing More-than-Human Blockchain Futures for the Food Commons. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Number 305. Association for Computing Machinery, 1-17. http://doi.org/10.1145/3411764.3445655 · DOI
  77. Nico Herbig, Santanu Pal, Josef van Genabith, and Antonio Krüger. 2019. Multi- Modal Approaches for Post-Editing Machine Translation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI '19). Association for Computing Machinery, New York, NY, USA, 1-11. https://doi.org/10.1145/3290605.3300461 · DOI
  78. Tom Hitron, Yoav Orlev, Iddo Wald, Ariel Shamir, Hadas Erel, and Oren Zuck- erman. 2019. Can children understand machine learning concepts? The effect of uncovering black boxes. In Proceedings of the 2019 CHI conference on human factors in computing systems. 1-11.
  79. Zoë Hitzig. 2020. The normative gap: mechanism design and ideal theories of justice. Economics & Philosophy 36, 3 (2020), 407-434.
  80. Stefan Holmlid. 2012. Participative; co-operative; emancipatory: From participa- tory design to service design. In Conference Proceedings ServDes. 2009; DeThink- ing Service; ReThinking Design; Oslo Norway 24-26 November 2009. Linköping University Electronic Press, 105-118.
  81. Naja Holten Møller, Irina Shklovski, and Thomas T Hildebrandt. 2020. Shifting concepts of value: Designing algorithmic decision-support systems for pub- lic services. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society. 1-12.
  82. Karen Holtzblatt and Hugh Beyer. 1997. Contextual design: defining customer- centered systems. Elsevier.
  83. Dirk Hovy, Taylor Berg-Kirkpatrick, Ashish Vaswani, and Eduard Hovy. 2013. Learning whom to trust with MACE. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1120-1130.
  84. Modassir Iqbal, Katie Shilton, Mahmoud F. Sayed, Douglas Oard, Jonah Lynn Rivera, and William Cox. [n. d.]. Search with Discretion: Value Sensitive Design of Training Data for Information Retrieval. 5 ([n. d.]), 1-20. Issue CSCW1. https://doi.org/10.1145/3449207 · DOI
  85. Maia Jacobs, Jeffrey He, Melanie F. Pradier, Barbara Lam, Andrew C. Ahn, Thomas H. McCoy, Roy H. Perlis, Finale Doshi-Velez, and Krzysztof Z. Gajos. 2021. Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Number 659. Association for Computing Machinery, 1-14. http://doi.org/10.1145/3411764.3445385 · DOI · Paper page
  86. Liu Jiang, Shixia Liu, and Changjian Chen. 2019. Recent research advances on interactive machine learning. Journal of Visualization 22, 2 (2019), 401-417.
  87. Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel Procaccia, and Christos- Alexandros Psomas. 2019. Statistical foundations of virtual democracy. In International Conference on Machine Learning. PMLR, 3173-3182.
  88. Simon Katan, Mick Grierson, and Rebecca Fiebrink. 2015. Using Interactive Machine Learning to Support Interface Development Through Workshops with Disabled People. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI '15). Association for Computing Machinery, New York, NY, USA, 251-254. https://doi.org/10. 1145/2702123.2702474 · DOI · Paper page
  89. Michael Katell, Meg Young, Dharma Dailey, Bernease Herman, Vivian Guetler, Aaron Tam, Corinne Bintz, Daniella Raz, and P. M. Krafft. 2020. Toward Situated Interventions for Algorithmic Equity: Lessons from the Field. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* '20). Association for Computing Machinery, New York, NY, USA, 45-55. https://doi.org/10.1145/3351095.3372874 · DOI · Paper page
  90. Harmanpreet Kaur, Eytan Adar, Eric Gilbert, and Cliff Lampe. 2022. Sensible AI: Re-imagining interpretability and explainability using sensemaking theory. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. 702-714.
  91. Jasmine Kaur, Rishabh Devgon, and Dhairya Chaudhary. [n. d.]. Work of Fiction: Using Speculative Design to Deliberate on the Future of Hiring. In Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing (New York, NY, USA, 2022-11-08) (CSCW'22 Companion). Association for Computing Machinery, 23-26. https://doi.org/10.1145/3500868. 3559442 · DOI
  92. Claire Kayacik, Sherol Chen, Signe Noerly, Jess Holbrook, Adam Roberts, and Douglas Eck. 2019. Identifying the intersections: User experience+ research scientist collaboration in a generative machine learning interface. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. 1-8.
  93. William R. Kearns, Neha Kaura, Myra Divina, Cuong Vo, Dong Si, Teresa Ward, and Weichao Yuwen. 2020. A Wizard-of-Oz Interface and Persona-Based Method- ology for Collecting Health Counseling Dialog. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI EA '20). Association for Computing Machinery, New York, NY, USA, 1-9. https://doi.org/10.1145/3334480.3382902 · DOI
  94. Christopher Kelty. 2016. Too Much Democracy in All the Wrong Places: Toward a Grammar of Participation. Current Anthropology 58 (12 2016), S000-S000. https://doi.org/10.1086/688705 · DOI
  95. Maaike Kleinsmann and Rianne Valkenburg. 2008. Barriers and enablers for creating shared understanding in co-design projects. Design Studies 29, 4 (2008), 369-386. https://doi.org/10.1016/j.destud.2008.03.003 · DOI
  96. Tomoko Komatsu, Marisela Gutierrez Lopez, Stephann Makri, Colin Porlezza, Glenda Cooper, Andrew MacFarlane, and Sondess Missaoui. [n. d.]. AI should embody our values: Investigating journalistic values to inform AI technol- ogy design. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society (New York, NY, USA, 2020- 10-26) (NordiCHI '20). Association for Computing Machinery, 1-13. https: //doi.org/10.1145/3419249.3420105 · DOI
  97. P. M. Krafft, Meg Young, Michael Katell, Jennifer E. Lee, Shankar Narayan, Micah Epstein, Dharma Dailey, Bernease Herman, Aaron Tam, Vivian Guetler, Corinne Bintz, Daniella Raz, Pa Ousman Jobe, Franziska Putz, Brian Robick, and Bissan Barghouti. 2021. An Action-Oriented AI Policy Toolkit for Technology Audits by Community Advocates and Activists. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Virtual Event, Canada) (FAccT '21). Association for Computing Machinery, New York, NY, USA, 772-781. https://doi.org/10.1145/3442188.3445938 · DOI
  98. Kavita Krishnaswamy. 2017. Participatory design: repositioning, transferring, and personal care robots. In Proceedings of the companion of the 2017 acm/ieee international conference on human-robot interaction. 351-352.
  99. Kavita Krishnaswamy, Srinivas Moorthy, and Tim Oates. 2017. Preliminary Sur- vey Analysis in Participatory Design: Repositioning, Transferring, and Personal Care Robots. In Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. 171-172.
  100. Bogdan Kulynych, David Madras, Smitha Milli, Inioluwa Deborah Raji, Angela Zhou, and Richard Zemel. 2020. Participatory Approaches to Machine Learning. International Conference on Machine Learning Workshop.
  101. Christopher A. Le Dantec and Sarah Fox. 2015. Strangers at the Gate: Gaining Access, Building Rapport, and Co-Constructing Community-Based Research. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (Vancouver, BC, Canada) (CSCW '15). Association for Computing Machinery, New York, NY, USA, 1348-1358. https://doi.org/10. 1145/2675133.2675147 · DOI · Paper page
  102. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436-444.
  103. Min Kyung Lee, Daniel Kusbit, Anson Kahng, Ji Tae Kim, Xinran Yuan, Allissa Chan, Daniel See, Ritesh Noothigattu, Siheon Lee, Alexandros Psomas, et al. 2019. WeBuildAI: Participatory framework for algorithmic governance. Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (2019), 1-35.
  104. Min Kyung Lee, Ishan Nigam, Angie Zhang, Joel Afriyie, Zhizhen Qin, and Sicun Gao. 2021. Participatory Algorithmic Management: Elicitation Methods for Worker Well-Being Models. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (New York, NY, USA, 2021-07-21) (AIES '21). Association for Computing Machinery, 715-726. https://doi.org/10.1145/3461702.3462628 · DOI
  105. Michael Lempert and E Summerson Carr. 2016. Scale: Discourse and dimensions of social life. University of California Press.
  106. Xingjun Li, Yuanxin Wang, Hong Wang, Yang Wang, and Jian Zhao. 2021. NBSearch: Semantic Search and Visual Exploration of Computational Notebooks. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1-14.
  107. Q Vera Liao, Daniel Gruen, and Sarah Miller. 2020. Questioning the AI: informing design practices for explainable AI user experiences. In Proceedings of the 2020 CHI conference on human factors in computing systems. 1-15.
  108. Phoebe Lin and Jessica Van Brummelen. 2021. Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Number 239. Association for Computing Machinery, 1-12. http://doi.org/10.1145/3411764.3445377 · DOI
  109. Gabrielle Kaili-May Liu. 2023. Perspectives on the Social Impacts of Reinforce- ment Learning with Human Feedback. arXiv preprint arXiv:2303.02891 (2023). · arXiv
  110. Daria Loi, Thomas Lodato, Christine T Wolf, Raphael Arar, and Jeanette Blomberg. 2018. PD manifesto for AI futures. In Proceedings of the 15th Par- ticipatory Design Conference: Short Papers, Situated Actions, Workshops and Tutorial-Volume 2. 1-4.
  111. Daria Loi, Christine T Wolf, Jeanette L Blomberg, Raphael Arar, and Margot Brereton. 2019. Co-designing AI futures: Integrating AI ethics, social computing, and design. In Companion Publication of the 2019 on Designing Interactive Systems Conference 2019 Companion. 381-384.
  112. Matti Luhtala, Santeri Saarinen, Laura Virkki, Johanna Lappi-Ramula, Hans Hokka, Helen Tran, Matti Savolainen, Ilkka Heinonen, Sami Kanerva, Juha Rajala, et al. 2018. Proactive Rescuework by Enhancing Situational Awareness: Modeling Resources, Services and People. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems. 1-8.
  113. C. B Macpherson. 1973. Democratic theory: essays in retrieval. Clarendon Press. OCLC: 606090.
  114. Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wort- man Vaughan, and Hanna Wallach. 2022. Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support. Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (2022), 1-26.
  115. Peter Mambrey, Gloria Mark, and Uta Pankoke-Babatz. 1998. User advocacy in participatory design: Designers' experiences with a new communication channel. Computer Supported Cooperative Work (CSCW) 7, 3 (1998), 291-313.
  116. Ji-Ye Mao, Karel Vredenburg, Paul W Smith, and Tom Carey. 2005. The state of user-centered design practice. Commun. ACM 48, 3 (2005), 105-109.
  117. Donald Martin Jr, Vinodkumar Prabhakaran, Jill Kuhlberg, Andrew Smart, and William S Isaac. 2020. Participatory problem formulation for fairer ma- chine learning through community based system dynamics. arXiv preprint arXiv:2005.07572 (2020). · arXiv
  118. Milagros Miceli and Julian Posada. 2022. The Data-Production Dispositif. Pro- ceedings of the ACM on Human-Computer Interaction 6, CSCW2 (2022), 1-37.
  119. Milagros Miceli, Martin Schuessler, and Tianling Yang. 2020. Between subjec- tivity and imposition: Power dynamics in data annotation for computer vision. Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020), 1-25.
  120. Milagros Miceli, Tianling Yang, Adriana Alvarado Garcia, Julian Posada, Sonja Mei Wang, Marc Pohl, and Alex Hanna. [n. d.]. Documenting Data Production Processes: A Participatory Approach for Data Work. 6 ([n. d.]), 510:1-510:34. Issue CSCW2. https://doi.org/10.1145/3555623 · DOI · Paper page
  121. Henrik Mucha, Dennis Mevißen, Sebastian Robert, Ricarda Jacobi, Kirsten Meyer, Winfried Heusler, and Daniel Arztmann. 2020. Co-Design Futures for AI and Space: A Workbook Sprint. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI EA '20). Association for Computing Machinery, New York, NY, USA, 1-8. https://doi. org/10.1145/3334480.3375203 · DOI
  122. Michael J Muller and Allison Druin. 2012. Participatory design: the third space in human-computer interaction. CRC Press, 1125-1153.
  123. Michael J Muller and Sarah Kuhn. 1993. Participatory design. Commun. ACM 36, 6 (1993), 24-28.
  124. Dylan Mulvin. 2021. Proxies: The cultural work of standing in. MIT Press.
  125. Yuri Nakao and Yusuke Sugano. [n. d.]. Use of Machine Learning by Non- Expert DHH People: Technological Understanding and Sound Perception. In Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society (Tallinn Estonia, 2020-10-25). ACM, 1- 12. https://doi.org/10.1145/3419249.3420157 · DOI
  126. Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, Timi Fasubaa, Tajudeen Kolawole, Taiwo Fagbohungbe, Solomon Oluwole Akinola, Sham- suddeen Hassan Muhammad, Salomon Kabongo, Salomey Osei, et al. 2020. Participatory research for low-resourced machine translation: A case study in african languages. arXiv preprint arXiv:2010.02353 (2020). · arXiv
  127. Isabel Neto, Hugo Nicolau, and Ana Paiva. 2021. Community Based Robot Design for Classrooms with Mixed Visual Abilities Children. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Number 31. Association for Computing Machinery, 1-12. http://doi.org/10.1145/3411764.3445135 · DOI
  128. Laura J Neumann and Susan Leigh Star. 1996. Making infrastructure: The dream of a common language. In Participatory Design Conference, Vol. 4. 231-240.
  129. Ritesh Noothigattu, Snehalkumar Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, and Ariel Procaccia. 2018. A voting-based system for ethical decision making. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.
  130. Judith S Olson and Wendy A Kellogg. 2014. Ways of Knowing in HCI. Vol. 2. Springer. [162]
  131. David Owen and Graham Smith. 2015. Survey article: Deliberation, democracy, and the systemic turn. Journal of Political Philosophy 23, 2 (2015), 213-234.
  132. Victoria Palacin, Matti Nelimarkka, Pedro Reynolds-Cuéllar, and Christoph Becker. 2020. The design of pseudo-participation. In Proceedings of the 16th Participatory Design Conference 2020-Participation (s) Otherwise-Volume 2. 40-44.
  133. Lawrence A Palinkas, Sarah M Horwitz, Carla A Green, Jennifer P Wisdom, Naihua Duan, and Kimberly Hoagwood. 2015. Purposeful sampling for quali- tative data collection and analysis in mixed method implementation research. Administration and policy in mental health and mental health services research 42 (2015), 533-544.
  134. Hyanghee Park, Daehwan Ahn, Kartik Hosanagar, and Joonhwan Lee. 2022. Designing Fair AI in Human Resource Management: Understanding Tensions Surrounding Algorithmic Evaluation and Envisioning Stakeholder-Centered Solutions. In CHI Conference on Human Factors in Computing Systems. 1-22.
  135. Joon Sung Park, Karrie Karahalios, Niloufar Salehi, and Motahhare Eslami. [n. d.]. Power Dynamics and Value Conflicts in Designing and Maintaining Socio-Technical Algorithmic Processes. 6 ([n. d.]), 110:1-110:21. Issue CSCW1. https://doi.org/10.1145/3512957 · DOI
  136. Sunjeong Park and Youn-kyung Lim. 2020. Investigating User Expectations on the Roles of Family-shared AI Speakers. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 1-13. https://doi.org/10.1145/3313831.3376450 · DOI
  137. Desmond U Patton, William R Frey, Kyle A McGregor, Fei-Tzin Lee, Kathleen McKeown, and Emanuel Moss. 2020. Contextual analysis of social media: The promise and challenge of eliciting context in social media posts with natural language processing. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 337-342.
  138. Jens Pedersen. 2016. War and peace in codesign. CoDesign 12 (2016), 171 -184.
  139. Francesca Polletta. 2012. Freedom is an endless meeting. In Freedom Is an Endless Meeting. University of Chicago Press.
  140. Aare Puussaar, Kyle Montague, Sean Peacock, Thomas Nappey, Robert Anderson, Jennine Jonczyk, Peter Wright, and Philip James. [n. d.]. SenseMyStreet: Sensor Commissioning Toolkit for Communities. 6 ([n. d.]), 324:1-324:26. Issue CSCW2. https://doi.org/10.1145/3555215 · DOI
  141. Organizers Of Queerinai, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J Sutherland, Davide Locatelli, Eva Breznik, Filip Klu- bicka, Hang Yuan, et al. 2023. Queer In AI: A Case Study in Community-Led Participatory AI. In Proceedings of the 2023 ACM Conference on Fairness, Ac- countability, and Transparency. 1882-1895.
  142. Lauge Baungaard Rasmussen. 2004. Action research-Scandinavian experiences. AI & SOCIETY 18, 1 (2004), 21-43.
  143. Daniella Raz, Corinne Bintz, Vivian Guetler, Aaron Tam, Michael Katell, Dharma Dailey, Bernease Herman, PM Krafft, and Meg Young. 2021. Face Mis-ID: An Interactive Pedagogical Tool Demonstrating Disparate Accuracy Rates in Facial Recognition. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. 895-904.
  144. People + AI Research. 2020. Boundary objects for participatory machine learning: PAIR Symposium 2020 Recap. https://medium.com/people-ai-research/pair- symposium-2020-recap-b7f206583e42 [179People + AI Research. 2021. PAIR @ Chi 2021. https://medium.com/people-ai- research/pair-chi-2021-410323929162
  145. Samantha Robertson and Niloufar Salehi. 2020. What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design. arXiv preprint arXiv:2007.06718 (2020). · arXiv
  146. Emma J Rose and Elin A Björling. 2017. Designing for engagement: using par- ticipatory design to develop a social robot to measure teen stress. In Proceedings of the 35th ACM International Conference on the Design of Communication. 1-10.
  147. Joanna Saad-Sulonen, Amalia De Götzen, Nicola Morelli, and Luca Simeone. 2020. Service design and participatory design: time to join forces?. In Proceedings of the 16th Participatory Design Conference 2020-Participation (s) Otherwise-Volume 2. 76-81.
  148. Selma Šabanović, Wan-Ling Chang, Casey C Bennett, Jennifer A Piatt, and David Hakken. 2015. A robot of my own: participatory design of socially assistive robots for independently living older adults diagnosed with depression. In International conference on human aspects of it for the aged population. Springer, 104-114.
  149. Debjani Saha, Candice Schumann, Duncan Mcelfresh, John Dickerson, Michelle Mazurek, and Michael Tschantz. 2020. Measuring non-expert comprehension of machine learning fairness metrics. In International Conference on Machine Learning. PMLR, 8377-8387.
  150. Elizabeth B.-N. Sanders and Pieter Jan Stappers. 2008. Co-creation and the new landscapes of design. CoDesign 4, 1 (2008), 5-18. https://doi.org/10.1080/ 15710880701875068 · DOI · Paper page
  151. Devansh Saxena and Shion Guha. 2020. Conducting Participatory Design to Improve Algorithms in Public Services: Lessons and Challenges. Association for Computing Machinery, New York, NY, USA, 383-388. https://doi.org/10.1145/ 3406865.3418331 · DOI · Paper page
  152. Laura Schelenz, Ivano Bison, Matteo Busso, Amalia de Götzen, Daniel Gatica- Perez, Fausto Giunchiglia, Lakmal Meegahapola, and Salvador Ruiz-Correa. 2021. The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (New York, NY, USA, 2021-07-21) (AIES '21). Association for Computing Machinery, 905-915. https://doi.org/10.1145/3461702.3462595 · DOI
  153. Daniel Schiff, Bogdana Rakova, Aladdin Ayesh, Anat Fanti, and Michael Lennon. 2020. Principles to practices for responsible AI: closing the gap. arXiv preprint arXiv:2006.04707 (2020). · arXiv
  154. Kristen M Scott, Sonja Mei Wang, Milagros Miceli, Pieter Delobelle, Karolina Sztandar-Sztanderska, and Bettina Berendt. 2022. Algorithmic Tools in Public Employment Services: Towards a Jobseeker-Centric Perspective. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 2138-2148.
  155. Hugo Scurto, Baptiste Caramiaux, and Frederic Bevilacqua. 2021. Prototyping Machine Learning Through Diffractive Art Practice. In Designing Interactive Systems Conference 2021 (New York, NY, USA, 2021-06-28) (DIS '21). Association for Computing Machinery, 2013-2025. https://doi.org/10.1145/3461778.3462163 · DOI
  156. Nick Seaver. 2021. Care and scale: decorrelative ethics in algorithmic recom- mendation. Cultural Anthropology 36, 3 (2021), 509-537.
  157. Elizabeth Seger, Aviv Ovadya, Ben Garfinkel, Divya Siddarth, and Allan Dafoe. 2023. Democratising AI: Multiple Meanings, Goals, and Methods. arXiv preprint arXiv:2303.12642 (2023). · arXiv
  158. Cathrine Seidelin, Yvonne Dittrich, and Erik Grönvall. 2020. Co-designing data experiments: Domain experts' exploration and experimentation with self- selected data sources. In Proceedings of the 11th Nordic Conference on Human- Computer Interaction: Shaping Experiences, Shaping Society. 1-11.
  159. Amartya Sen. 1977. Social choice theory: A re-examination. Econometrica: journal of the Econometric Society (1977), 53-89.
  160. Amartya Sen. 1986. Social choice theory. Handbook of mathematical economics 3 (1986), 1073-1181.
  161. Hong Shen, Wesley H Deng, Aditi Chattopadhyay, Zhiwei Steven Wu, Xu Wang, and Haiyi Zhu. 2021. Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 850-861.
  162. Hong Shen, Haojian Jin, Ángel Alexander Cabrera, Adam Perer, Haiyi Zhu, and Jason I Hong. 2020. Designing Alternative Representations of Confusion Matri- ces to Support Non-Expert Public Understanding of Algorithm Performance. Proceedings of the ACM on Human-Computer Interaction 4, CSCW2 (2020), 1-22.
  163. Ben Shneiderman. 2021. Human-Centered AI. Issues in Science and Technology 37, 2 (2021), 56-61.
  164. Jesper Simonsen and Toni Robertson. 2012. Routledge international handbook of participatory design. Routledge.
  165. Zoe Skinner, Stacey Brown, and Greg Walsh. 2020. Children of Color's Percep- tions of Fairness in AI: An Exploration of Equitable and Inclusive Co-Design. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 1-8.
  166. Mona Sloane, Emanuel Moss, Olaitan Awomolo, and Laura Forlano. 2020. Partic- ipation is not a design fix for machine learning. arXiv preprint arXiv:2007.02423 (2020). · arXiv
  167. Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Choulde- chova, Ken Holstein, Zhiwei Steven Wu, and Haiyi Zhu. 2022. Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 1162-1177.
  168. Marc Steen. 2013. Co-Design as a Process of Joint Inquiry and Imagination. Design Issues 29, 2 (2013), 16-28. http://www.jstor.org/stable/24266991
  169. Elias Storms, Oscar Alvarado, and Luciana Monteiro-Krebs. [n. d.]. 'Trans- parency is Meant for Control' and Vice Versa: Learning from Co-designing and Evaluating Algorithmic News Recommenders. 6 ([n. d.]), 405:1-405:24. Issue CSCW2. https://doi.org/10.1145/3555130 · DOI
  170. Hariharan Subramonyam, Colleen Seifert, and Eytan Adar. 2021. Towards A Process Model for Co-Creating AI Experiences. In Designing Interactive Systems Conference 2021. Association for Computing Machinery, 1529-1543. http://doi.org/10.1145/3461778.3462012 · DOI
  171. Harini Suresh, Rajiv Movva, Amelia Lee Dogan, Rahul Bhargava, Isadora Cruxen, Angeles Martinez Cuba, Guilia Taurino, Wonyoung So, and Catherine D'Ignazio. 2022. Towards Intersectional Feminist and Participatory ML: A Case Study in Supporting Feminicide Counterdata Collection. In 2022 ACM Conference on Fairness, Accountability, and Transparency. 667-678.
  172. Giovanni Maria Troiano, Matthew Wood, and Casper Harteveld. 2020. "And This, Kids, Is How I Met Your Mother": Consumerist, Mundane, and Uncanny Futures with Sex Robots. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, 1-17. http://doi.org/10.1145/3313831.3376598 · DOI
  173. Anna Lowenhaupt Tsing. 2012. On NonscalabilityThe Living World Is Not Amenable to Precision-Nested Scales. Common Knowledge 18, 3 (2012), 505- 524.
  174. Kim M Unertl, Chris L Schaefbauer, Terrance R Campbell, Charles Senteio, Katie A Siek, Suzanne Bakken, and Tiffany C Veinot. 2016. Integrating community-based participatory research and informatics approaches to im- prove the engagement and health of underserved populations. Journal of the American Medical Informatics Association 23, 1 (2016), 60-73.
  175. Daryoush Vaziri, David Golchinfar, Gunnar Stevens, and Dirk Schreiber. [n. d.]. Exploring Future Work -Co-Designing a Human-robot Collaboration Envi- ronment for Service Domains. In Proceedings of the 2020 ACM Designing In- teractive Systems Conference. Association for Computing Machinery, 153-164. http://doi.org/10.1145/3357236.3395483 · DOI
  176. Salomé Viljoen, Jake Goldenfein, and Lee McGuigan. 2021. Design choices: Mechanism design and platform capitalism. Big Data & Society 8, 2 (2021), 20539517211034312.
  177. John Vines, Rachel Clarke, Peter Wright, John McCarthy, and Patrick Olivier. 2013. Configuring Participation: On How We Involve People in Design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Paris, France) (CHI '13). Association for Computing Machinery, New York, NY, USA, 429-438. https://doi.org/10.1145/2470654.2470716 · DOI
  178. John Vines, Peter C. Wright, David Silver, Maggie Winchcombe, and Patrick Olivier. 2015. Authenticity, Relatability and Collaborative Approaches to Shar- ing Knowledge about Assistive Living Technology. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (Vancouver, BC, Canada) (CSCW '15). Association for Computing Machinery, New York, NY, USA, 82-94. https://doi.org/10.1145/2675133.2675222 · DOI
  179. Nina B Wallerstein and Bonnie Duran. 2006. Using community-based participa- tory research to address health disparities. Health promotion practice 7, 3 (2006), 312-323.
  180. Greg Walsh and Eric Wronsky. 2019. AI + Co-Design: Developing a Novel Computer-Supported Approach to Inclusive Design. In Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Com- puting (Austin, TX, USA) (CSCW '19). Association for Computing Machinery, New York, NY, USA, 408-412. https://doi.org/10.1145/3311957.3359456 · DOI
  181. Christine Wolf and Jeanette Blomberg. [n. d.]. Evaluating the Promise of Human- Algorithm Collaborations in Everyday Work Practices. 3 ([n. d.]), 143:1-143:23. Issue CSCW. https://doi.org/10.1145/3359245 · DOI · Paper page
  182. Christine T. Wolf, Haiyi Zhu, Julia Bullard, Min Kyung Lee, and Jed R. Brubaker. 2018. The Changing Contours of "Participation" in Data-Driven, Algorithmic Ecosystems: Challenges, Tactics, and an Agenda. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (Jersey City, NJ, USA) (CSCW '18). Association for Computing Machinery, New York, NY, USA, 377-384. https://doi.org/10.1145/3272973.3273005 · DOI
  183. Allison Woodruff, Sarah E Fox, Steven Rousso-Schindler, and Jeffrey Warshaw. 2018. A qualitative exploration of perceptions of algorithmic fairness. In Pro- ceedings of the 2018 CHI conference on human factors in computing systems. 1-14.
  184. Julia Woodward, Zari McFadden, Nicole Shiver, Amir Ben-hayon, Jason C Yip, and Lisa Anthony. 2018. Using co-design to examine how children conceptualize intelligent interfaces. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1-14.
  185. Deepika Yadav, Prerna Malik, Kirti Dabas, and Pushpendra Singh. 2019. Feed- pal: Understanding Opportunities for Chatbots in Breastfeeding Education of Women in India. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 170 (nov 2019), 30 pages. https://doi.org/10.1145/3359272 · DOI · Paper page
  186. 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 Technology 21, 2 (2019), 89-103.
  187. Bowen Yu, Ye Yuan, Loren Terveen, Zhiwei Steven Wu, Jodi Forlizzi, and Haiyi Zhu. 2020. Keeping designers in the loop: Communicating inherent algorithmic trade-offs across multiple objectives. In Proceedings of the 2020 ACM Designing Interactive Systems Conference. 1245-1257.
  188. Yunfeng Zhang, Rachel Bellamy, and Kush Varshney. 2020. Joint optimization of AI fairness and utility: A human-centered approach. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 400-406.
  189. Zheng Zhang, Ying Xu, Yanhao Wang, Bingsheng Yao, Daniel Ritchie, Tong- shuang Wu, Mo Yu, Dakuo Wang, and Toby Jia-Jun Li. 2022. StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement. In CHI Conference on Human Factors in Computing Systems. 1-21.

Authors
Delgado, F., Yang, S., Madaio, M., & Yang, Q.
Affiliations
  • Cornell University (US)
  • University of Southern California (US)
  • Google (United States) (US)
ORCID
Venue
Equity and Access in Algorithms, Mechanisms, and Optimization
Publisher
ACM
Published
2023-10-30
Pages
1-23
DOI
10.1145/3617694.3623261
OpenAlex
W4388007985
Cited by
256
References
159
Citations by year
2026: 109 · 2025: 93 · 2024: 53 · 2023: 1
FWCI
64.14
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords
participatory design, AI design, stakeholder engagement, agency, conceptual framework, political theory, technology design

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