  The aggregation of multiple opinions plays a crucial role in decision-making,
such as in hiring and loan review, and in labeling data for supervised
learning. Although majority voting and existing opinion aggregation models are
effective for simple tasks, they are inappropriate for tasks without
objectively true labels in which disagreements may occur. In particular, when
voter attributes such as gender or race introduce bias into opinions, the
aggregation results may vary depending on the composition of voter attributes.
A balanced group of voters is desirable for fair aggregation results but may be
difficult to prepare. In this study, we consider methods to achieve fair
opinion aggregation based on voter attributes and evaluate the fairness of the
aggregated results. To this end, we consider an approach that combines opinion
aggregation models such as majority voting and the Dawid and Skene model (D&S
model) with fairness options such as sample weighting. To evaluate the fairness
of opinion aggregation, probabilistic soft labels are preferred over discrete
class labels. First, we address the problem of soft label estimation without
considering voter attributes and identify some issues with the D&S model. To
address these limitations, we propose a new Soft D&S model with improved
accuracy in estimating soft labels. Moreover, we evaluated the fairness of an
opinion aggregation model, including Soft D&S, in combination with different
fairness options using synthetic and semi-synthetic data. The experimental
results suggest that the combination of Soft D&S and data splitting as a
fairness option is effective for dense data, whereas weighted majority voting
is effective for sparse data. These findings should prove particularly valuable
in supporting decision-making by human and machine-learning models with
balanced opinion aggregation.
