The definition and implementation of fairness in automated decisions has been
extensively studied by the research community. Yet, there hides fallacious
reasoning, misleading assertions, and questionable practices at the foundations
of the current fair machine learning paradigm. Those flaws are the result of a
failure to understand that the trade-off between statistically accurate
outcomes and group similar outcomes exists as independent, external constraint
rather than as a subjective manifestation as has been commonly argued. First,
we explain that there is only one conception of fairness present in the fair
machine learning literature: group similarity of outcomes based on a sensitive
attribute where the similarity benefits an underprivileged group. Second, we
show that there is, in fact, a trade-off between statistically accurate
outcomes and group similar outcomes in any data setting where group disparities
exist, and that the trade-off presents an existential threat to the equitable,
fair machine learning approach. Third, we introduce a proof-of-concept
evaluation to aid researchers and designers in understanding the relationship
between statistically accurate outcomes and group similar outcomes. Finally,
suggestions for future work aimed at data scientists, legal scholars, and data
ethicists that utilize the conceptual and experimental framework described
throughout this article are provided.

该研究讨论了在自动化决策中公平性的概念和实现方式，并说明了当前的公平机器学习范式中存在的错误推理、误导性陈述和可疑做法，并探讨了在存在群体差异的任何数据设置中，统计上准确结果和群体相似结果之间的权衡存在，并对未来的相关领域提出了建议。

公平机器学习的错误基础

The Flawed Foundations of Fair Machine Learning

In this work, we study group recommendation in a particular scenario, namely
Occasional Group Recommendation (OGR). Most existing works have addressed OGR
by aggregating group members' personal preferences to learn the group
representation. However, the representation learning for a group is most
complex beyond the fusion of group member representation, as the personal
preferences and group preferences may be in different spaces. In addition, the
learned user representation is not accurate due to the sparsity of users'
interaction data. Moreover, the group similarity in terms of common group
members has been overlooked, which however has the great potential to improve
the group representation learning. In this work, we focus on addressing the
above challenges in group representation learning task, and devise a
hierarchical hyperedge embedding-based group recommender, namely HyperGroup.
Specifically, we propose to leverage the user-user interactions to alleviate
the sparsity issue of user-item interactions, and design a GNN-based
representation learning network to enhance the learning of individuals'
preferences from their friends' preferences, which provides a solid foundation
for learning groups' preferences. To exploit the group similarity to learn a
more accurate group representation from highly limited group-item interactions,
we connect all groups as a network of overlapping sets, and treat the task of
group preference learning as embedding hyperedges in a hypergraph, where an
inductive hyperedge embedding method is proposed. To further enhance the
group-level preference modeling, we develop a joint training strategy to learn
both user-item and group-item interactions in the same process. We conduct
extensive experiments on two real-world datasets and the experimental results
demonstrate the superiority of our proposed HyperGroup in comparison to the
state-of-the-art baselines.

本文提出了一种基于超边嵌入的层次化组推荐算法 HyperGroup，在考虑群组内部成员相似性，通过利用用户间的交互信息来解决用户关系稀疏性问题，同时结合 GNN 实现个人与群组的表示学习，通过构建超图和超边嵌入技术，增强了群组内的相似性，在两个真实数据集上的实验结果证明了该算法的优越性。