mlsanityData to 7 Oct 2026

Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation

arXiv 2302.11623 ↗First posted 22 Feb 2023cs.HCcited by 17

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Abstract (arXiv)

Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help stakeholders surface ways to improve and make fairer decision-making processes. We created "Deliberating with AI", a web tool that enables people to create and evaluate ML models in order to examine strengths and shortcomings of past decision-making and deliberate on how to improve future decisions. We apply this tool to a context of people selection, having stakeholders -- decision makers (faculty) and decision subjects (students) -- use the tool to improve graduate school admission decisions. Through our case study, we demonstrate how the stakeholders used the web tool to create ML models that they used as boundary objects to deliberate over organization decision-making practices. We share insights from our study to inform future research on stakeholder-centered participatory AI design and technology for organizational decision-making.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2023.

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Citing papers per year: 2023: 1; 2024: 9; 2025: 5; 2026: 2.

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  1. 2024Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Makingcited by 38
  2. 2024Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questionscited by 21
  3. 2024Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipediacited by 18
  4. 2024Position: Towards Bidirectional Human-AI Alignmentcited by 15
  5. 2024"This is not a data problem": Algorithms and Power in Public Higher Education in Canadacited by 6
  6. 2024Studying Up Public Sector AI: How Networks of Power Relations Shape Agency Decisions Around AI Design and Usecited by 6
  7. 2024Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Makingcited by 5
  8. 2024EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholderscited by 5
  9. 2024Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviorscited by 5
  10. 2023Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AIcited by 3
  11. 2025Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Makingcited by 2
  12. 2025From Prediction to Foresight: The Role of AI in Designing Responsible Futurescited by 2
5 more
  1. 2025Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspectivecited by 1
  2. 2025Towards Meaningful Transparency in Civic AI Systemscited by 1
  3. 2026CoDesignAI: An AI-Enabled Multi-Agent, Multi-User System for Collaborative Urban Design at the Conceptual Stagecited by 1
  4. 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessmentcited by 0
  5. 2026Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Toolscited by 0