Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making

• URL: http://arxiv.org/abs/2502.08542v2
• Date: Tue, 15 Jul 2025 14:22:31 GMT
• Title: Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making
• Authors: Vittoria Vineis, Giuseppe Perelli, Gabriele Tolomei,
• Abstract summary: We propose a participatory framework that reframes decision-making as a multi-stakeholder optimization problem.<n>Our modular, model-agnostic framework employs k-fold cross-validation to fine-tune user-provided prediction models.<n>A synthetic scoring mechanism aggregates user-defined preferences across multiple metrics to rank strategies.
• Score: 3.3044728148521623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional automated decision-support systems, often based on supervised learning, focus on predicting outcomes to recommend actions. However, they typically overlook the complexity of multi-actor environments, where diverse and conflicting stakeholder preferences must be balanced. At the same time, participatory AI approaches remain largely context-specific, limiting their broader applicability. To address these gaps, we propose a participatory framework that reframes decision-making as a multi-stakeholder optimization problem, using context-dependent reward functions to represent each actor's preferences. Our modular, model-agnostic framework employs k-fold cross-validation to fine-tune user-provided prediction models and evaluate decision strategies, including compromise functions that mediate stakeholder trade-offs. A synthetic scoring mechanism aggregates user-defined preferences across multiple metrics to rank strategies and select an optimal decision-maker for generating actionable recommendations on new data. Validated on two high-stake real-world case studies, the framework consistently produces stakeholder-aware decisions that outperform purely predictive baselines across multiple metrics, while enhancing the transparency and accountability of AI-supported decision-making.

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