# Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" arXiv: https://arxiv.org/abs/2111.01122 ยท HTML: https://mlsanity.com/paper/2111.01122/ ## Key facts - Cited by 26 papers in the map (data through 14 Oct 2026). - Builds on 6 of its 61 references that are in the map. - First posted on arXiv 1 Nov 2021. ## Builds on (most cited first, 6 of 6) - [Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics](https://mlsanity.com/paper/2005.07572/) (2020) - [Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness](https://mlsanity.com/paper/2010.05434/) (2020) - [A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare](https://mlsanity.com/paper/2107.03487/) (2021) - [Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems](https://mlsanity.com/paper/2001.04879/) (2020) - [What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design](https://mlsanity.com/paper/2007.06718/) (2020) - [Mechanism Design for Social Good](https://mlsanity.com/paper/1810.09832/) (2018) ## Built on by (most cited first, 26 of 26) - [AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia](https://mlsanity.com/paper/2305.11844/) (2023) - [Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services](https://mlsanity.com/paper/2303.09743/) (2023) - [Seeing Like a Toolkit: How Toolkits Envision the Work of AI Ethics](https://mlsanity.com/paper/2202.08792/) (2022) - [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024) - [Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences](https://mlsanity.com/paper/2310.04621/) (2023) - [Seamful XAI: Operationalizing Seamful Design in Explainable AI](https://mlsanity.com/paper/2211.06753/) (2022) - [Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience](https://mlsanity.com/paper/2302.10395/) (2023) - [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023) - [Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders](https://mlsanity.com/paper/2205.08928/) (2022) - [Scaling Laws Do Not Scale](https://mlsanity.com/paper/2307.03201/) (2023) - [Going public: the role of public participation approaches in commercial AI labs](https://mlsanity.com/paper/2306.09871/) (2023) - [Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI](https://mlsanity.com/paper/2308.07213/) (2023) - [Think About the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance](https://mlsanity.com/paper/2207.01482/) (2022) - [Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Use](https://mlsanity.com/paper/2405.12458/) (2024) - [Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents](https://mlsanity.com/paper/2310.15065/) (2023) - [Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education](https://mlsanity.com/paper/2311.05792/) (2023) - [From Fake Perfects to Conversational Imperfects: Exploring Image-Generative AI as a Boundary Object for Participatory Design of Public Spaces](https://mlsanity.com/paper/2411.00949/) (2024) - [Multi-Group Proportional Representation in Retrieval](https://mlsanity.com/paper/2407.08571/) (2024) - [Operationalizing the Blueprint for an AI Bill of Rights: Recommendations for Practitioners, Researchers, and Policy Makers](https://mlsanity.com/paper/2407.08689/) (2024) - [Integrating ESG and AI: A Comprehensive Responsible AI Assessment Framework](https://mlsanity.com/paper/2408.00965/) (2024) - [The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation](https://mlsanity.com/paper/2502.03038/) (2025) - [Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Study](https://mlsanity.com/paper/2602.14357/) (2026) - [Reimagining AI in Social Work: Practitioner Perspectives on Incorporating Technology in their Practice](https://mlsanity.com/paper/2407.10244/) (2024) - [BiaSWE: An Expert Annotated Dataset for Misogyny Detection in Swedish](https://mlsanity.com/paper/2502.07637/) (2025) - [Information Access of the Oppressed: Freirean Design for Emancipatory Information Access](https://mlsanity.com/paper/2601.09600/) (2026) - [A Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance](https://mlsanity.com/paper/2609.16592/) (2026) ## Abstract (arXiv) There is a growing consensus in HCI and AI research that the design of AI systems needs to engage and empower stakeholders who will be affected by AI. However, the manner in which stakeholders should participate in AI design is unclear. This workshop paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices via a survey of recent published research and a dozen semi-structured interviews with AI researchers and practitioners. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design and articulates a set of empirical findings that in ensemble detail out the contemporary landscape of participatory practice in AI design. These findings can help bootstrap a more principled discussion on how PD of AI should move forward across AI, HCI, and other research communities.