A User-Centric Exploration of Axiomatic Explainable AI in Participatory Budgeting

Explainable Artificial Intelligence (XAI) has been widely used to clarify the opaque nature of AI systems. One area where XAI has gained significant attention is Participatory Budgeting (PB). PB mechanisms aim to achieve a proper allocation concerning both the votes collected based on user's preferences and the budget. An essential criterion for evaluating these mechanisms is their ability to satisfy desired properties known as axioms. However, even though there are complex voting rules that meet some axioms, concerns regarding transparency persist. In this study, we propose an approach to provide explanations in a PB setting by treating axioms as constraints and seeking outcomes that adhere to these constraints. This method enhances system transparency and explainability. Each potential allocation is accepted or rejected based on whether it satisfies the axioms, and the linear nature of the axioms reduces computational complexity. We evaluated our approach with real-world users to assess its effectiveness and helpfulness. Our pilot study shows that users generally find explanations helpful for understanding the system's decisions and perceive the outcomes as fairer. Additionally, users prefer general explanations over counterfactual ones.