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.

通过与参与者互动并利用参与者的动机值来估计其价值偏好，解决参与者选择和动机之间的不一致性，并通过直接与参与者互动的消歧策略改善个体价值偏好的估计。

混合参与式系统中的价值偏好估计和消歧

Value Preferences Estimation and Disambiguation in Hybrid Participatory  Systems

Partial label learning (PLL) is a typical weakly supervised learning problem
in which each instance is associated with a candidate label set, and among
which only one is true. However, the assumption that the ground-truth label is
always among the candidate label set would be unrealistic, as the reliability
of the candidate label sets in real-world applications cannot be guaranteed by
annotators. Therefore, a generalized PLL named Unreliable Partial Label
Learning (UPLL) is proposed, in which the true label may not be in the
candidate label set. Due to the challenges posed by unreliable labeling,
previous PLL methods will experience a marked decline in performance when
applied to UPLL. To address the issue, we propose a two-stage framework named
Unreliable Partial Label Learning with Recursive Separation (UPLLRS). In the
first stage, the self-adaptive recursive separation strategy is proposed to
separate the training set into a reliable subset and an unreliable subset. In
the second stage, a disambiguation strategy is employed to progressively
identify the ground-truth labels in the reliable subset. Simultaneously,
semi-supervised learning methods are adopted to extract valuable information
from the unreliable subset. Our method demonstrates state-of-the-art
performance as evidenced by experimental results, particularly in situations of
high unreliability.

针对部分标签学习中标签可能不可靠的情况，提出了一种名为 UPLLRS 的两阶段框架，其中自适应递归分离策略用于训练数据分成可靠和不可靠子集，消除不可靠的标签后在可靠子集上进行半监督学习。该方法在实验结果方面表现出最先进的性能，特别适用于高度不可靠的情况。