PWL #62: Racial Equity in Algorithmic Criminal Justice

Details
• What we'll do
Mini Paper: Shashwat will present The Tail at Scale https://cseweb.ucsd.edu/~gmporter/classes/fa17/cse124/post/schedule/p74-dean.pdf
SYSTEMS THAT RESPOND to user actions quickly (within 100ms) feel more fluid and natural to users than those that take longer. Improvements in Internet connectivity and the rise of warehouse-scale computing systems have enabled Web services that provide fluid responsiveness while consulting multi-terabyte datasets spanning thousands of servers; for example, the Google search system updates query results interactively as the user types, predicting the most likely query based on the prefix typed so far, performing the search and showing the results within a few tens of milliseconds. Emerging augmented-reality devices (such as the Google Glass prototype) will need associated Web services with even greater responsiveness in order to guarantee seamless interactivity.

Main Event: Max will present Racial Equity in Algorithmic Criminal Justice (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3144831)
Algorithmic tools for predicting violence and criminality are being used more and more in policing, bail, and sentencing. Scholarly attention to date has focused on their procedural due process implications. My aim here is to consider these instruments’ interaction with the enduring racial legacies of the criminal justice system There are two competing lenses for evaluating the racial effects of algorithmic criminal justice: constitutional doctrine and emerging technical standards of “algorithmic fairness.” I argue first that constitutional doctrine is poorly suited to the task. It will often fail to capture the full range of racial issues that potentially arise in the use of algorithmic tools in criminal justice. While the emerging technical standards of algorithmic fairness are at least fitted to the specifics of the relevant technology, the technical literature has failed to ask how various conceptions of fairness track (or fail to track) policy-significant consequences. Drawing on the technical literature, I propose a reformulated metric for considering racial equity considerations in algorithmic design. Rather than asking about abstract definitions of fairness, a criminal justice algorithm should be evaluated in terms of its long-term, dynamic effects on racial stratification. The metric of nondiscrimination for the algorithmic context should focus on the net burden placed on a racial minority. Surprisingly, a precise formulation of this metric suggests that it can converge with the socially efficient decision rule under certain conditions.

• Important to know

Meetup will be at this link:
https://meet.jit.si/paperswelove

This is last minute, but it's a rough time.

As a chapter of Papers We Love we abide by and enforce the PWL Code of Conduct (https://github.com/papers-we-love/seattle/blob/master/code-of-conduct.md) at our events. Please give it a read, plan on acting like an adult, and involve one of the organizers if you need help.

Stop slacking and join us in the #seattle channel at https://papersweloveslack.herokuapp.com!

If you have a paper you'd like to present, or even just a mini, please hit up one of the organizers :) We're always looking for more presenters.