Computer-based decision systems are widely used to automate decisions in many
aspects of everyday life, which include sensitive areas like hiring, loaning
and even criminal sentencing. A decision pipeline heavily relies on large
volumes of historical real-world data for training its models. However,
historical training data often contains gender, racial or other biases which
are propagated to the trained models influencing computer-based decisions. In
this work, we propose a robust methodology that guarantees the removal of
unwanted biases while maximally preserving classification utility. Our approach
can always achieve this in a model-independent way by deriving from real-world
data the asymptotic dataset that uniquely encodes demographic parity and
realism. As a proof-of-principle, we deduce from public census records such an
asymptotic dataset from which synthetic samples can be generated to train
well-established classifiers. Benchmarking the generalization capability of
these classifiers trained on our synthetic data, we confirm the absence of any
explicit or implicit bias in the computer-aided decision.

计算机决策系统在许多日常生活的方面得到广泛应用，但其中可能存在性别、种族或其他偏见问题。本研究提出一种鲁棒的方法，通过真实世界数据提取出表征人口平衡和真实性的数据集，用于训练分类器，并测试其泛化能力，证实计算机辅助决策中不存在明示或隐含偏见。