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, 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). We show also how discrimination can be decomposed into variance, bias, and noise. Finally, we challenge the commonly accepted mitigation approach that discrimination can be addressed by collecting more samples of the underrepresented group.

翻译：准确衡量歧视对于可靠评估训练后的机器学习模型的公平性至关重要。测量歧视中的任何偏差都会导致对现有差异的放大或低估。存在多种偏差来源，通常假设机器学习产生的偏差会平等地影响不同群体（例如女性与男性、白人与黑人等）。然而，若偏差对不同群体的影响程度不同，则可能加剧对特定子群体的歧视。"采样偏差"一词在文献中常被不一致地用于描述由采样过程引起的偏差。本文通过引入明确定义的采样偏差变体——即样本量偏差（SSB）和代表性不足偏差（URB），尝试厘清该术语的含义。我们还展示了歧视如何可分解为方差、偏差和噪声。最后，我们挑战了普遍接受的缓解方法，即通过收集更多代表性不足群体的样本即可解决歧视问题。