Data Education: Policing


Data Education: Policing

From Victoria Bernal

In the week of 2A focussed on power, politics, and authority I have them read Ben Penglase's article on security in a Rio favela and I usually show part of the film "STOP-Challenging New York City's Stop and Frisk Law." 

 

 From Lindsay Poirer

On NYC and CompStat - the statistical program that proceeded stop and frisk (versions of CompStat were also used on Baltimore and Chicago): 

 Smith, Chris. 2018. “The Crime-Fighting Program That Changed New York Forever.” Intelligencer. March 2, 2018. http://nymag.com/intelligencer/2018/03/the-crime-fighting-program-that-changed-new-york-forever.html.

Part of this narrative is that what was considered successful policing in NY became all about gaming the stats ...so of course officers started to manipulate the stats because they were afraid of losing their jobs:

https://fivethirtyeight.com/features/how-the-nypd-abused-citizens-in-the-name-of-data-and-how-one-cop-exposed-it-all/

On Stop and Frisk: 

Note that NYCLU pushing to open the stop and frisk database was a key factor in deeming stop and frisk unconstitutional. The statistics were the most powerful argument in the supreme court case - showing that minorities were stopped at dramatically higher rates, and this very rarely resulted in an arrest or weapon found 

12 years of data from New York City suggest stop-and-frisk wasn’t that effective

New York Civil Liberties Union - Annual Stop and Frisk Data Analysis

I also have an R Notebook where I walk students step by step through analyzing NYC's stop and frisk dataset and helping them interpret the missing information, the incorrect information, as well as the useful information. 

Then I also assign some things around predictive policing:

https://www.bloomberg.com/news/videos/2015-03-05/the-rise-of-predictive-policing-tech

Lum, Kristian, and William Isaac. 2016.  "To predict and serve?."

Williams, Bärí A. 2018. “Opinion | ‘Intelligent’ Policing and My Innocent Children.” The New York Times, January 20, 2018, sec. Opinion. https://www.nytimes.com/2017/12/02/opinion/sunday/intelligent-policing-and-my-innocent-children.html.

And Cathy O'Neil has a chapter on this in Weapons of Math Destruction

...and there is so much more right now on predictive policing, I think the hardest part is narrowing it down to the most useful/relevant. The links above are good for undergrads. 

You might also think about expanding beyond just policing to also include the issues around sentencing and parole:

Barry-Jester, Anna Maria. 2015. “Should Prison Sentences Be Based On Crimes That Haven’t Been Committed Yet?” FiveThirtyEight (blog). August 4, 2015. https://fivethirtyeight.com/features/prison-reform-risk-assessment/.

Carlson, Alyssa. 2017. “The Need for Transparency in the Age of Predictive Sentencing Algorithms.” Iowa Law Review. 2017. https://ilr.law.uiowa.edu/print/volume-103-issue-1/the-need-for-transparency-in-the-age-of-predictive-sentencing-algorithms/.

Park, Andrew Lee. 2019. “Injustice Ex Machina: Predictive Algorithms in Criminal Sentencing.” UCLA Law Review. February 19, 2019. https://www.uclalawreview.org/injustice-ex-machina-predictive-algorithms-in-criminal-sentencing/.

There's also some really great data-stories around prison-based gerry-mandering. Have I told you about this? In some states, prisoners are counted in the census where they are being incarcerated rather than where their homes are. This dramatically raises the population in certain districts (even though those incarcerated cannot vote), which some states have used to their advantage to redraw districts. 

https://www.youtube.com/watch?v=gh0xZVM7TPg

However, the Prison Policy Initiative has done really great mapping and data analysis to help build a case against this: https://www.prisonersofthecensus.org/

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