In addition to reproducing discriminatory relationships in the training data, machine learning (ML) systems can also introduce or amplify discriminatory effects. We refer to this as introduced unfairness, and investigate the conditions under which it may arise. To this end, we propose introduced total variation as a measure of introduced unfairness, and establish graphical conditions under which it may be incentivised to occur. These criteria imply that adding the sensitive attribute as a feature removes the incentive for introduced variation under well-behaved loss functions. Additionally, taking a causal perspective, introduced path-specific effects shed light on the issue of when specific paths should be considered fair.

Racial disparities in US drug arrest rates have been observed for decades, but their causes and policy implications are still contested. Some have argued that the disparities largely reflect differences in drug use between racial groups, while others have hypothesized that discriminatory enforcement policies and police practices play a significant role. In this work, we analyze racial disparities in the enforcement of marijuana violations in the US. Using data from the National Incident-Based Reporting System (NIBRS) and the National Survey on Drug Use and Health (NSDUH) programs, we investigate whether marijuana usage and purchasing behaviors can explain the racial composition of offenders in police records. We examine potential driving mechanisms behind these disparities and the extent to which county-level socioeconomic factors are associated with corresponding disparities. Our results indicate that the significant racial disparities in reported incidents and arrests cannot be explained by differences in marijuana days-of-use alone. Variations in the location where marijuana is purchased and in the frequency of these purchases partially explain the observed disparities. We observe an increase in racial disparities across most counties over the last decade, with the greatest increases in states that legalized the use of marijuana within this timeframe. Income, high school graduation rate, and rate of employment positively correlate with larger racial disparities, while the rate of incarceration is negatively correlated. We conclude with a discussion of the implications of the observed racial disparities in the context of algorithmic fairness.