  Classification, a heavily-studied data-driven machine learning task, drives
an increasing number of prediction systems involving critical human decisions
such as loan approval and criminal risk assessment. However, classifiers often
demonstrate discriminatory behavior, especially when presented with biased
data. Consequently, fairness in classification has emerged as a high-priority
research area. Data management research is showing an increasing presence and
interest in topics related to data and algorithmic fairness, including the
topic of fair classification. The interdisciplinary efforts in fair
classification, with machine learning research having the largest presence,
have resulted in a large number of fairness notions and a wide range of
approaches that have not been systematically evaluated and compared. In this
paper, we contribute a broad analysis of 13 fair classification approaches and
additional variants, over their correctness, fairness, efficiency, scalability,
robustness to data errors, sensitivity to underlying ML model, data efficiency,
and stability using a variety of metrics and real-world datasets. Our analysis
highlights novel insights on the impact of different metrics and high-level
approach characteristics on different aspects of performance. We also discuss
general principles for choosing approaches suitable for different practical
settings, and identify areas where data-management-centric solutions are likely
to have the most impact.
