Big Data Surveillance: The Case of Policing | Sarah Brayne

Abstract

This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities.

  1. First, discretionary assessments of risk are supplemented and quantified using risk scores.
  2. Second, data are used for predictive, rather than reactive or explanatory, purposes.
  3. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people.
  4. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact.
  5. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions.

Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.

Conclusions

Through a case study of the Los Angeles Police Department, this article analyzed the role of big data in surveillance practices. By socially situating big data, I examined why it was adopted, how it is used, and what the implications of its use are. Focusing on the interplay between surveillance practices, law, and technology offers new insights into social control and inequality. I argued that big data participates in and reflects existing social structures. Far from eliminating human discretion and bias, big data represents a new form of capital that is both a social product and a social resource. What data law enforcement collects, their methods for analyzing and interpreting it, and the way it informs their practice are all part of a fundamentally social process. Characterizing predictive models as “just math,” and fetishizing computation as an objective process, obscures the social side of algorithmic decision-making. Individuals’ interpretation of data occurs in preexisting institutional, legal, and social settings, and it is through that interpretive process that power dynamics come into play. Use of big data has the potential to ameliorate discriminatory practices, but these findings suggest implementation is of paramount importance. As organizational theory and literature from science and technology studies suggests, when new technology is overlaid onto an old organizational structure, longstanding problems shape themselves to the contours of the new technology, and new unintended consequences are generated. The process of transforming individual actions into “objective” data raises fundamentally sociological questions that this research only begins to address. In many ways, it transposes classic concerns from the sociology of quantification about simplification, decontextualization, and the privileging of measurable complex social phenomena onto the big data landscape. Surveillance is always ambiguous; it is implicated in both social inclusion and exclusion, and it creates both opportunities and constraints. The way in which surveillance helps achieve organizational goals and structure life chances may differ according to the individuals and institutions involved. Examining the means of big data surveillance across institutional domains is an open and timely line of inquiry, because once a new technology is disseminated in an institutional setting, it is difficult to scale back.

Mentions

  • Palantir
    • PredPol
    • Automatic License Plate Reader (ALPR)
    • Automatic Vehicle Locator (AVL)
    • Enterprise Master Person Index (EMPI)
  • Los Angeles Police Department (LAPD)
  • Big Data
  • risk scores
  • Smart Policiing Initiative
  • Department of Homeland Security (DHS)
  • Federal Bureau of Investigations (FBI)
  • Central Intelligence Agency (CIA)
  • Immigration and Customs Enforcement (ICE)
  • Los Angeles County Sheriff’s Department (LASD)
  • Fusion Center
  • JRIC
  • Real-Time Crime Analysis Center (RACR)
  • Crime Intelligence Detail (CID)
  • Operation LASER (Los Angeles’ Strategic Extraction and Restoration
  • Parole Compliance Unit
  • Field Fnterview (FI)
    program)

Argot

  • surveillant assemblage
  • trigger mechanism
  • in the system (to be ‘in the system’)
  • net widening
  • decontextualization
Origin
(at least)
  • Deleuze
  • Guattari
  • Marx; no: not that guy; the other one.
  • Pasquale

Quotes

<quote>Originally
intended for use in national defense, Palantir was initially partially funded by In-Q-Tel, the CIA’s venture capital firm. Palantir now has government and commercial customers, including the CIA, FBI, ICE, LAPD, NYPD, NSA, DHS, and J.P. Morgan. JRIC (the Southern California fusion center) started using Palantir in 2009, with the LAPD following shortly after. The use of Palantir has expanded rapidly through the Department, with regular training sessions and more divisions signing on each year. It has also spread throughout the greater L.A. region: in 2014, Palantir won the Request for Proposals to implement the statewide AB 109 administration program, which involves data integration and monitoring of the post-release community supervision population.</quote>

<quote>When I asked an officer to provide examples of why he stops people with high point values, he replied:

Yesterday this individual might have got stopped because he jaywalked. Today he mighta got stopped because he didn’t use his turn signal or whatever the case might be. So that’s two points . . . you could conduct an investigation or if something seems out of place you have your consensual stops.[7] So a pedestrian stop, this individual’s walking, “Hey, can I talk to you for a moment?” “Yeah what’s up?” You know, and then you just start filling out your card as he answers questions or whatever. And what it was telling us is who is out on the street, you know, who’s out there not neces- sarily maybe committing a crime but who’s active on the streets. You put the activity of . . . being in a street with maybe their violent background and one and one might create the next crime that’s gonna occur.

Promotions

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How the Personal Data Extraction Industry Ends | Doc Searls

Doc Searls; How the Personal Data Extraction Industry Ends; In His Blog, centrally hosted on Medium; 2017-08-28; originally tracked as bitly:dtxtrcn inbound to His Blog; 2017-08-27.

tl;dr → The advertising industry has been outlawed by GDPR.

Occasion

Who Owns the Internet? — What Big Tech’s Monopoly Powers Mean for our Culture; Elizabeth Kolbert (bio); In The New Yorker; 2017-08-28; separately filled.
Teaser: What Big Tech’s monopoly powers mean for our culture.
tl;dr → promotes the books

Mentioned

  • Use of Google Trends,to highlight interest in the terms
    • GDPR.
    • Big Data
    • IBM
    • McKinsey
    • SAP
    • Hewlett-Packard (HP);
      which? HPE or HPQ?
    • Oracle
    • Microsoft
    • Gartner
  • Vendor-defined terms
  • Customer-defined terms.

Offenders

  • Amazon, is good
  • Apple, is good
  • Facebook, is bad
  • Google, is bad.
  • Netflix, is bad.

Argot

  • B2B
  • B2C
  • C2B
  • C2C, a.k.a. a stich-N-bitch, a coffee klatch, a tailgate;
    supplied for completeness; of course that’s a thing.
  • egology, a neologism
    defined as an amplification of monopoly; frustration is expressed.
  • epiphenomenal, is a word.

Referenced

Also

Previously

Centrally hosted on Medium, and elsewhere…

Actualities

separately noted

The Myth of the Skills Gap | MIT Technology Review

The Myth of the Skills Gap; Andrew Weaver; In MIT Technology Review; 2017-08-25.
Teaser: The idea that American workers are being left in the dust because they lack technological savvy does not stand up to scrutiny. Our focus should be on coordination and communication between workers and employers.

Andrew Weaver is an assistant professor in the School of Labor and Employment Relations at the University of Illinois at Urbana-Champaign.

tl;dr → There is no hiring problem. There is no “skills gap.” Entry-level IT desk jobs require but “password reset” type skill sets. Don’t worry about the robots.

Mentions

  • skill surveys
  • occupations
    • manufacturing production workers
    • IT help-desk technicians
    • laboratory technologists
  • Science, Technology, Engineering, Mathematics (STEM)

Quotes

  • <quote>We would ultimately like to ratchet up both employer skill requirements and employee skill levels (and the corresponding productivity and wages), but doing so requires that we think not only about adjusting worker skill levels, but also about changing employer behavior.</quote>
  • <quote>However, pushing students and new workers to unilaterally make expensive investments in generic skill categories (or, worse, to just get “more education”) is likely to result in inefficient investments, mistaken choices, and a large number of dead-end paths.</quote>
  • <quote>A final point is worth making on technology and the fear that robots will steal all the jobs. Occupations evolve as technology advances. Help-desk technicians once spent more time on tasks like password resets than they do today. Despite the automation of such functions, computer problems—and the occupation that tackles them—continue to expand.</quote>

Soup

  • skills
  • economic growth
  • unproductive hand-wringing
  • blinkered focus
  • supply side
  • labor market
  • workers
  • STEM
  • math
  • high-tech establishments
  • cutting-edge establishments
  • soft skills
  • entry-level
  • skills gap
  • economists and labor-market experts
  • probability
  • statistics
  • algebra
  • labor-market intermediaries
  • employment agencies
  • trade associations (unions)
  • employer relationships

Referenced

Previously

In MIT Technology Review

Why surveillance marketers don’t worry about GDPR (but privacy nerds should) | Don Marti

Don Marti; Why surveillance marketers don’t worry about GDPR (but privacy nerds should); In His Blog; 2017-08-01.

tl;dr → His point, and he does have one is… not shown. Frustration is exhibited.

Referenced

Jimi Wales’ Wiki

Previously

In His Blog

  • Playing for Third Place; WHEN?
    tl;dr → <styled>right-wing factions who are better at surveillance marketing than they are</styled>

Cookieless Identification and Tracking of Devices | OPC, CA

Christopher Parsons (OPC, CA); Privacy Tech-Know Blog: Cookieless Identification and Tracking of Devices; In Their Blog; 2017-08-21.
OPC is the Office of the Privacy Commissioner of Canada

tl;dr → a recitation, a tutorial, no new material.

Mentions

  • Fingerprinting
  • passive
  • active
  • Cookie-like
    • local storage
    • HTML5
    • Flash
    • Silverlight
    • etc.
  • Cookie-free
    • that would be “the fingerprinting”

Argot

  • Behavioral Targeted Advertising (OBA)
  • Virtual Private Network (VPN)

Referenced

Previously

In Their Blog, on Their Site

Corporate Surveillance in Everyday Life | Cracked Labs


Corporate Surveillance in Everyday Life. How Companies Collect, Combine, Analyze, Trade, and Use Personal Data on BillionsWolfie Christl,; Cracked Labs, Vienna; 2017-06; 93 pages.

Teaser: <shrill>How thousands of companies monitor, analyze, and influence the lives of billions. Who are the main players in today’s digital tracking? What can they infer from our purchases, phone calls, web searches, and Facebook likes? How do online platforms, tech companies, and data brokers collect, trade, and make use of personal data?</shrill>

Table of Contents

  1. Background and Scope
  2. Introduction
  3. Relevant players within the business of personal data
    1. Businesses in all industries
    2. Media organizations and digital publishers
    3. Telecom companies and Internet Service Providers
    4. Devices and Internet of Things
    5. Financial services and insurance
    6. Public sector and key societal domains
    7. Future developments?
  4. The Risk Data Industry
    1. Rating people in finance, insurance and employment
    2. Credit scoring based on digital behavioral data
    3. Identity verification and fraud prevention
    4. Online identity and fraud scoring in real-time
    5. Investigating consumers based on digital records
  5. The Marketing Data Industry
    1. Sorting and ranking consumers for marketing
    2. The rise of programmatic advertising technology
    3. Connecting offline and online data
    4. Recording and managing behaviors in real-time
    5. Collecting identities and identity resolution
    6. Managing consumers with CRM, CIAM and MDM
  6. Examples of Consumer Data Broker Ecosystems
    1. Acxiom, its services, data providers, and partners
    2. Oracle as a consumer data platform
    3. Examples of data collected by Acxiom and Oracle
  7. Key Developments in Recent Years
    1. Networks of digital tracking and profiling
    2. Large-scale aggregation and linking of identifiers
    3. “Anonymous” recognition
    4. Analyzing, categorizing, rating and ranking people
    5. Real-time monitoring of behavioral data streams
    6. Mass personalization
    7. Testing and experimenting on people
    8. Mission creep – everyday life, risk assessment and marketing
  8. Conclusion
  9. Figures
  10. References

Mentions

Separately noted.

America’s dark economic secret: How income stripping & corporate inversion, as gimmicks, has wages and jobs hanging by a thread | Salon

America’s dark economic secret: How a giant gimmick has wages and jobs hanging by a thread; ; In Salon; 2014-09-24.
Teaser: Welcome to the “gimmick economy” — where it’s not about good products or labor, but something far more scary

David Dayen has a book forthcoming
Chain of Title: How Three Ordinary Americans Uncovered Wall Street’s Great Foreclosure Fraud; New Press; 2016-04-14; 320 pages; kindle: no, paper: $20+SHT.

Occasion

Mentions

  • gimmick economy, a neologism of David Dayen, the reporter.

Who

  • Jack Lew, Treasury Secretary, United States

Referenced

In archaeological order

Undated?