Entity matching (EM) is a challenging problem studied by different
communities for over half a century. Algorithmic fairness has also become a
timely topic to address machine bias and its societal impacts. Despite
extensive research on these two topics, little attention has been paid to the
fairness of entity matching.
Towards addressing this gap, we perform an extensive experimental evaluation
of a variety of EM techniques in this paper. We generated two social datasets
from publicly available datasets for the purpose of auditing EM through the
lens of fairness. Our findings underscore potential unfairness under two common
conditions in real-world societies: (i) when some demographic groups are
overrepresented, and (ii) when names are more similar in some groups compared
to others. Among our many findings, it is noteworthy to mention that while
various fairness definitions are valuable for different settings, due to EM's
class imbalance nature, measures such as positive predictive value parity and
true positive rate parity are, in general, more capable of revealing EM
unfairness.

通过大规模实验证明了实体匹配算法的公平性存在潜在的不足，特别在现实社会中出现一些特定条件下的人口群体过多代表性和名字相似度不同的群体之间。

公正视角下的实体匹配实验分析与评估

Through the Fairness Lens: Experimental Analysis and Evaluation of  Entity Matching

Smooth and seamless robot navigation while interacting with humans depends on
predicting human movements. Forecasting such human dynamics often involves
modeling human trajectories (global motion) or detailed body joint movements
(local motion). Prior work typically tackled local and global human movements
separately. In this paper, we propose a novel framework to tackle both tasks of
human motion (or trajectory) and body skeleton pose forecasting in a unified
end-to-end pipeline. To deal with this real-world problem, we consider
incorporating both scene and social contexts, as critical clues for this
prediction task, into our proposed framework. To this end, we first couple
these two tasks by i) encoding their history using a shared Gated Recurrent
Unit (GRU) encoder and ii) applying a metric as loss, which measures the source
of errors in each task jointly as a single distance. Then, we incorporate the
scene context by encoding a spatio-temporal representation of the video data.
We also include social clues by generating a joint feature representation from
motion and pose of all individuals from the scene using a social pooling layer.
Finally, we use a GRU based decoder to forecast both motion and skeleton pose.
We demonstrate that our proposed framework achieves a superior performance
compared to several baselines on two social datasets.

本文提出了一种新的框架来处理机器人导航中预测人类动态的问题，该框架将人类运动（或轨迹）和骨骼姿态预测这两个任务统一起来，并考虑到了场景和社交上下文等多种因素，最终在两个社交数据集上表现优异。