By Nicolas S. Rothbacher

Predictive policing has quickly become widespread in the United States. Practitioners claim it can greatly increase police efficiency and base decisions on objective statistics. Critics say that these algorithms reproduce discriminatory outcomes in a biased justice system. In this thesis, I investigate fare enforcement in New York City and what might happen if predictive policing were applied. First I analyze legal precedents on discrimination law to create a framework for understanding whether policy is legally discriminatory. In this framework the fairness of a government policy is judged based on how different groups are treated by the process of carrying out the policy. Three elements must be examined: a comparison group that is treated fairly, discriminatory burden for the disadvantaged group, and government negligence or intent. Next, using this framework, I perform data analysis on fare evasion arrests in New York City, and find evidence of discrimination. Finally, I examine predictive policing to determine what its effect on fare enforcement might be. I conclude that predictive policing algorithms trained on the arrests will be ineffective and seen as unfair due to the institutional practices that impact the data. This examination sheds light on how machine learning fairness could be analyzed using societal expectations of fairness.

Cambridge, MA: Massachusetts Institute of Technology m Institute for Data, Systems, and Society, 2020 . 54p.

By William Cai, Johann Gaebler, Justin Kaashoek, Lisa Pinals, Samuel Madden, Sharad Goel

Past studies have found that racial and ethnic minorities are more likely than White drivers to be pulled over by the police for alleged traffic infractions, including a combination of speeding and equipment violations. It has been difficult, though, to measure the extent to which these disparities stem from discriminatory enforcement rather than from differences in offense rates. Here, in the context of speeding enforcement, we address this challenge by leveraging a novel source of telematics data, which include second-by-second driving speed for hundreds of thousands of individuals in 10 major cities across the United States. We find that time spent speeding is approximately uncorrelated with neighborhood demographics, yet, in several cities, officers focused speeding enforcement in small, demographically nonrepresentative areas. In some cities, speeding enforcement was concentrated in predominantly non-White neighborhoods, while, in others, enforcement was concentrated in predominately White neighborhoods. Averaging across the 10 cities we examined, and adjusting for observed speeding behavior, we find that speeding enforcement was moderately more concentrated in non-White neighborhoods. Our results show that current enforcement practices can lead to inequities across race and ethnicity.

PNAS Nexus, Volume 1, Issue 4, September 2022, 

By Aliza Hochman Bloom

In December of 2019, a woman was robbed in Jersey City, and she quickly reported it to a 911-dispatcher. When the dispatcher asked her whether the suspect was “Black, white or Hispanic,” she responded that she did not know. But when relaying the description to a police officer, the dispatcher improperly added to the woman’s account that the suspect was a “Black male.” This error appears to have been inadvertent, a mistake reflecting the pernicious implicit bias linking Blackness with criminality. William L. Scott subsequently challenged the constitutionality of the police stop leading to his arrest, arguing that the improper injection of race into the be-on-the-lookout (BOLO) description violated New Jersey’s constitutional guarantee of equal protection under the law. The appellate court agreed, emphasizing the importance of “deterring discriminatory policing in all of its permutations,” and suppressed all evidence obtained from the unlawful stop.

Though purporting to prohibit racial discrimination in policing, Equal Protection doctrine has not halted the racialized selection process funneling our criminal legal system. Meanwhile, the Fourth Amendment has been interpreted in a way that facilitates racially disproportionate policing. Scholars have hoped that courts could allow selective enforcement claims that include officers’ implicit racial bias. Within this treacherous doctrinal landscape, Sate v. Scott did just that.

Scott is the first court to hold that evidence of implicit racial bias in policing establishes a prima facie case of racial discrimination justifying the exclusion of evidence. But the remedy that it used to deter future police misconduct—suppression of evidence—is unlikely to deter implicit bias. And the court’s praiseworthy desire to halt racist policing rests on unproven assumptions—including that bias training could ameliorate implicit racial bias. Recognizing that present doctrine permits policing decisions that yield systemically racist outcomes, this article argues that courts should adopt an outcome focused approach, where discretionary policing decisions that result in consistently racialized results are scrutinized without requiring proof of discriminatory intent.

University of Illinois Law Review (forthcoming 2025), 50pg