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

arXiv: https://arxiv.org/abs/2302.11623 · HTML: https://mlsanity.com/paper/2302.11623/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2023.

## Built on by (most cited first, 17 of 17)

- [Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making](https://mlsanity.com/paper/2403.16812/) (2024)
- [Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions](https://mlsanity.com/paper/2404.11023/) (2024)
- [Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia](https://mlsanity.com/paper/2402.14147/) (2024)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- ["This is not a data problem": Algorithms and Power in Public Higher Education in Canada](https://mlsanity.com/paper/2403.13969/) (2024)
- [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)
- [Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making](https://mlsanity.com/paper/2403.01791/) (2024)
- [EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders](https://mlsanity.com/paper/2407.11442/) (2024)
- [Policy Maps: Tools for Guiding the Unbounded Space of LLM Behaviors](https://mlsanity.com/paper/2409.18203/) (2024)
- [Human-in-the-loop Fairness: Integrating Stakeholder Feedback to Incorporate Fairness Perspectives in Responsible AI](https://mlsanity.com/paper/2312.08064/) (2023)
- [Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making](https://mlsanity.com/paper/2502.08542/) (2025)
- [From Prediction to Foresight: The Role of AI in Designing Responsible Futures](https://mlsanity.com/paper/2511.21570/) (2025)
- [Adaptive Human-Agent Teaming: A Review of Empirical Studies from the Process Dynamics Perspective](https://mlsanity.com/paper/2504.10918/) (2025)
- [Towards Meaningful Transparency in Civic AI Systems](https://mlsanity.com/paper/2510.07889/) (2025)
- [CoDesignAI: An AI-Enabled Multi-Agent, Multi-User System for Collaborative Urban Design at the Conceptual Stage](https://mlsanity.com/paper/2603.16008/) (2026)
- ["I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment](https://mlsanity.com/paper/2509.17956/) (2025)
- [Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Tools](https://mlsanity.com/paper/2602.06984/) (2026)

## 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.