On the Origins of Sampling Bias: Implications on Fairness Measurement and Mitigation
This work addresses fairness measurement in machine learning, but it is incremental as it clarifies existing terminology rather than introducing a new paradigm.
The paper tackled the problem of ambiguous definitions of sampling bias in fairness measurement by introducing two clearly defined variants, sample size bias (SSB) and underrepresentation bias (URB), and conducted experiments on benchmark datasets to provide actionable recommendations for practitioners.
Accurately measuring discrimination is crucial to faithfully assessing fairness of trained machine learning (ML) models. Any bias in measuring discrimination leads to either amplification or underestimation of the existing disparity. Several sources of bias exist and it is assumed that bias resulting from machine learning is born equally by different groups (e.g. females vs males, whites vs blacks, etc.). If, however, bias is born differently by different groups, it may exacerbate discrimination against specific sub-populations. Sampling bias, in particular, is inconsistently used in the literature to describe bias due to the sampling procedure. In this paper, we attempt to disambiguate this term by introducing clearly defined variants of sampling bias, namely, sample size bias (SSB) and underrepresentation bias (URB). Through an extensive set of experiments on benchmark datasets and using mainstream learning algorithms, we expose relevant observations in several model training scenarios. The observations are finally framed as actionable recommendations for practitioners.