Locating disparities in machine learning
arxiv(2022)
摘要
Machine learning can provide predictions with disparate outcomes, in which
subgroups of the population (e.g., defined by age, gender, or other sensitive
attributes) are systematically disadvantaged. In order to comply with upcoming
legislation, practitioners need to locate such disparate outcomes. However,
previous literature typically detects disparities through statistical
procedures for when the sensitive attribute is specified a priori. This limits
applicability in real-world settings where datasets are high dimensional and,
on top of that, sensitive attributes may be unknown. As a remedy, we propose a
data-driven framework called Automatic Location of Disparities (ALD) which aims
at locating disparities in machine learning. ALD meets several demands from
industry: ALD (1) is applicable to arbitrary machine learning classifiers; (2)
operates on different definitions of disparities (e.g., statistical parity or
equalized odds); and (3) deals with both categorical and continuous predictors
even if disparities arise from complex and multi-way interactions known as
intersectionality (e. g., age above 60 and female). ALD produces interpretable
audit reports as output. We demonstrate the effectiveness of ALD based on both
synthetic and real-world datasets. As a result, we empower practitioners to
effectively locate and mitigate disparities in machine learning algorithms,
conduct algorithmic audits, and protect individuals from discrimination.
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关键词
algorithmic fairness,algorithmic bias,tree algorithm,recursive partitioning,machine learning
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