Due to the widespread use of data-powered systems in our everyday lives,
concepts like bias and fairness gained significant attention among researchers
and practitioners, in both industry and academia. Such issues typically emerge
from the data, which comes with varying levels of quality, used to train
supervised machine learning systems. With the commercialization and deployment
of such systems that are sometimes delegated to make life-changing decisions,
significant efforts are being made towards the identification and removal of
possible sources of data bias that may resurface to the final end user or in
the decisions being made. In this paper, we present research results that show
how bias in data affects end users, where bias is originated, and provide a
viewpoint about what we should do about it. We argue that data bias is not
something that should necessarily be removed in all cases, and that research
attention should instead shift from bias removal towards the identification,
measurement, indexing, surfacing, and adapting for bias, which we name bias
management.

本篇论文探讨了数据偏见对最终用户的影响、偏见的来源以及如何处理数据偏见的问题。提出了‘bias management’的概念，强调应该从识别、测量、索引、面向和调整偏见的角度入手，而不是在所有情况下都要消除数据偏见。

数据偏差管理

Data Bias Management

The lack of bias management in Recommender Systems leads to minority groups
receiving unfair recommendations. Moreover, the trade-off between equity and
precision makes it difficult to obtain recommendations that meet both criteria.
Here we propose a Deep Learning based Collaborative Filtering algorithm that
provides recommendations with an optimum balance between fairness and accuracy
without knowing demographic information about the users. Experimental results
show that it is possible to make fair recommendations without losing a
significant proportion of accuracy.

提出了一种基于深度学习协同过滤算法的推荐系统，实现了公平和准确度之间的最佳平衡，无需知道用户的人口统计信息。实验结果表明，可以在不失去大部分准确度的情况下进行公平的推荐。