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Big Data Surveillance: The Case of Policing - Sarah Brayne, 2017
Tracks the intertwined growth of surveillance and "big data" within the Los Angeles Police Department through interviews and observation. Identifies five areas of transformation:

1. discretionary risk assessments -> quantified risk assessment: data that is input to these assessments includes the results of "field interviews," creating a feedback loop since "high risk" individuals are stopped for interviews more frequently, but each police contact adds to a person's risk score

2. reactive/explanatory analysis -> predictive analysis (e.g. PredPol)

3. query-based -> alert-based systems: the main example here is Palantir, which allows you to set alerts based on individuals, locations, or other characteristics present in real-time (often high-frequency) databases. Queries themselves become data, as the fact that someone has been searched by other officers using the system can itself be flagged

4. lower database inclusion thresholds: law enforcement databases have expanded beyond individuals who have direct contact with police through arrests or stops -- they now include data collected during stop-and-frisk and risk-based field interviews, the field interview data can include information on who else was with the person of interest even though they did not have any direct police contact, automated license plate readers (ALPR) suck up data constantly

5. integration of different data systems -- merging data across data stores and creating unique identifiers across systems, which governments might be interested in for the purpose of improving service delivery, also transforms the nature of surveillance -- interviewees rave about their Palantir software which lets them see everything in one place. In addition to data from public agencies including law enforcement, social services, health/mental health services, child/family services, the paper also mentions Palantir's constant inclusion of new data sources -- repossession/collections agencies, social media, foreclosure, electronic toll data, utility bills, pay parking lots, fast food call data, university camera feeds, rebate data . . . "In some instances, it is simply eaasier for law enforcement to purchase privately collected data than to rely on in-house data because there are fewer constitutional protections . . .". Much of the newly integrated data suffers from related types of inclusion bias (e.g. your chances of appearing in stop-and-frisk data differs based on race and class, this is also true for usage of social health/family services, etc., and even the placement and usage of ALPRs is based on measured crime rates), so that in all, these systems come to define and mark a population as suspicious (the only responses to queries will be people already in the data in some way).
surveillance  machine-learning  big-data  police  police-data  palantir  lapd  los-angeles 
june 2019 by tarakc02
Uber, Lyft Drivers Nabbed in LAPD Stings Funded by Taxi Industry - NBC Southern California
The Los Angeles Police Department and the Los Angeles Department of Transportation conduct four to 10 stings per month to catch so-called "bandit taxi drivers."
uber  lyft  jitneys  lapd  police  sting 
march 2019 by po
LAPD Officers Who Shot 103 Rounds at Two Innocent Women Violated Policy - The Atlantic
It took almost a year, but a civilians oversight board has finally decided that the eight Los Angeles Police Department officers who shot over 100 rounds at two innocent women were not acting in accordance with department policy.
police.misconduct  lapd  police.shooting  police.violence  police.oversight  government  law.enforcement  failboat 
november 2018 by po
The LAPD Has a New Surveillance Formula, Powered by Palantir
"Because they over-patrol certain areas. If you’re only looking on Crenshaw and you’re only pulling Black people over then it’s only gonna make it look like, you know, whoever you pulled over or whoever you searched or whoever you criminalized that’s gonna be where you found something" - member of a focus group convened by the Stop LAPD Spying Coalition
police  police-data  predictive-policing  crime-data  criminal-justice  lapd  palantir  bias  disparate-impact  algorithmic-bias  algorithmic-abuse 
may 2018 by tarakc02
Palantir Knows Everything About You
an undercurrent of this that occurred while i read -- in the same way that cops now show up to black lives matter protests in surplus military gear and tanks, here is a story about tools designed to fight a war, being deployed domestically by employers against their employees and by police against citizens, nearly two decades into this endless war.

also this paragraph is amazing:

> As Thiel’s wealth has grown, he’s gotten more strident. In a 2009 essay for the Cato Institute, he railed against taxes, ­government, women, poor people, and society’s acquiescence to the inevitability of death. (Thiel doesn’t accept death as inexorable.) He wrote that he’d reached some radical conclusions: "Most importantly, I no longer believe that freedom and democracy are compatible." The 1920s was the last time one could feel “genuinely optimistic” about American democracy, he said; since then, "the vast increase in welfare beneficiaries and the extension of the franchise to women—two constituencies that are notoriously tough for libertarians—have rendered the notion of 'capitalist democracy' into an oxymoron."
privacy  surveillance  palantir  algorithmic-bias  algorithmic-abuse  lapd 
april 2018 by tarakc02
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january 2018 by DocDre
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december 2017 by richardorvince
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december 2017 by jeremydfranklin

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