Predictive risk models in the public sector are commonly developed using
administrative data that is more complete for subpopulations that more greatly
rely on public services. In the United States, for instance, information on
health care utilization is routinely available to government agencies for
individuals supported by Medicaid and Medicare, but not for the privately
insured. Critiques of public sector algorithms have identified such
differential feature under-reporting as a driver of disparities in algorithmic
decision-making. Yet this form of data bias remains understudied from a
technical viewpoint. While prior work has examined the fairness impacts of
additive feature noise and features that are clearly marked as missing, the
setting of data missingness absent indicators (i.e. differential feature
under-reporting) has been lacking in research attention. In this work, we
present an analytically tractable model of differential feature under-reporting
which we then use to characterize the impact of this kind of data bias on
algorithmic fairness. We demonstrate how standard missing data methods
typically fail to mitigate bias in this setting, and propose a new set of
methods specifically tailored to differential feature under-reporting. Our
results show that, in real world data settings, under-reporting typically leads
to increasing disparities. The proposed solution methods show success in
mitigating increases in unfairness.

本研究提出了一个分析可追踪的差异特征低报告模型，并用此模型来表征此类数据偏差对算法公平性的影响。我们证明在实际数据环境中，低报告通常导致不公平的增加，而我们提出的解决方案方法成功地减轻了不公平的增加。

差异特征未报告对算法公平性的影响

The Impact of Differential Feature Under-reporting on Algorithmic  Fairness

The combination of clinical judgement and predictive risk models crucially
assist social workers to segregate children at risk of maltreatment and decide
when authorities should intervene. Predictive risk modelling to address this
matter has been initiated by several governmental welfare authorities worldwide
involving administrative data and machine learning algorithms. While previous
studies have investigated risk factors relating to child maltreatment, several
gaps remain as to understanding how such risk factors interact and whether
predictive risk models perform differently for children with different
features. By integrating Principal Component Analysis and K-Means clustering,
this paper presents initial findings of our work on the identification of such
features as well as their potential effect on current risk modelling
frameworks. This approach allows examining existent, unidentified yet, clusters
of New Zealand (NZ) children reported with care and protection concerns, as
well as to analyse their inner structure, and evaluate the performance of
prediction models trained cluster wise. We aim to discover the extent of
clustering degree required as an early step in the development of predictive
risk models for child maltreatment and so enhance the accuracy of such models
intended for use by child protection authorities. The results from testing
LASSO logistic regression models trained on identified clusters revealed no
significant difference in their performance. The models, however, performed
slightly better for two clusters including younger children. our results
suggest that separate models might need to be developed for children of certain
age to gain additional control over the error rates and to improve model
accuracy. While results are promising, more evidence is needed to draw
definitive conclusions, and further investigation is necessary.

通过整合主成分分析和 K 均值聚类，本文初步发现了在目前风险建模框架中对特定特征进行辨识以及这些特征对其潜在影响的研究成果，并评估了按簇训练预测模型的性能。研究结果表明可能需要为特定年龄段的儿童开发单独的模型，以提高模型准确性。然而，还需要更多证据来得出明确的结论，需要进行进一步的调查。