  Understanding citizens' values in participatory systems is crucial for
citizen-centric policy-making. We envision a hybrid participatory system where
participants make choices and provide motivations for those choices, and AI
agents estimate their value preferences by interacting with them. We focus on
situations where a conflict is detected between participants' choices and
motivations, and propose methods for estimating value preferences while
addressing detected inconsistencies by interacting with the participants. We
operationalize the philosophical stance that "valuing is deliberatively
consequential." That is, if a participant's choice is based on a deliberation
of value preferences, the value preferences can be observed in the motivation
the participant provides for the choice. Thus, we propose and compare value
estimation methods that prioritize the values estimated from motivations over
the values estimated from choices alone. Then, we introduce a disambiguation
strategy that addresses the detected inconsistencies between choices and
motivations by directly interacting with the participants. We evaluate the
proposed methods on a dataset of a large-scale survey on energy transition. The
results show that explicitly addressing inconsistencies between choices and
motivations improves the estimation of an individual's value preferences. The
disambiguation strategy does not show substantial improvements when compared to
similar baselines--however, we discuss how the novelty of the approach can open
new research avenues and propose improvements to address the current
limitations.
