When using machine learning for automated prediction, it is important to
account for fairness in the prediction. Fairness in machine learning aims to
ensure that biases in the data and model inaccuracies do not lead to
discriminatory decisions. E.g., predictions from fair machine learning models
should not discriminate against sensitive variables such as sexual orientation
and ethnicity. The training data often in obtained from social surveys. In
social surveys, oftentimes the data collection process is a strata sampling,
e.g. due to cost restrictions. In strata samples, the assumption of
independence between the observation is not fulfilled. Hence, if the machine
learning models do not account for the strata correlations, the results may be
biased. Especially high is the bias in cases where the strata assignment is
correlated to the variable of interest. We present in this paper an algorithm
that can handle both problems simultaneously, and we demonstrate the impact of
stratified sampling on the quality of fair machine learning predictions in a
reproducible simulation study.

使用机器学习进行自动预测时，公平性对于预测是很重要的。本文提出了一种算法，可以同时处理数据分层抽样和公平机器学习预测质量的问题。

公平广义线性混合模型

Fair Generalized Linear Mixed Models

Experts advising decision-makers are likely to display expertise which varies
as a function of the problem instance. In practice, this may lead to
sub-optimal or discriminatory decisions against minority cases. In this work we
model such changes in depth and breadth of knowledge as a partitioning of the
problem space into regions of differing expertise. We provide here new
algorithms that explicitly consider and adapt to the relationship between
problem instances and experts' knowledge. We first propose and highlight the
drawbacks of a naive approach based on nearest neighbor queries. To address
these drawbacks we then introduce a novel algorithm - expertise trees - that
constructs decision trees enabling the learner to select appropriate models. We
provide theoretical insights and empirically validate the improved performance
of our novel approach on a range of problems for which existing methods proved
to be inadequate.

本文研究了专家与问题实例之间的知识差异，提出了一种新算法 —— 专业树，解决了现有方法在选择适当模型时存在的问题，从而提高了性能。