The Undue Influence of Surveillance Technology Companies on Policing | Elizabeth Joh

Elizabeth E. Joh; The Undue Influence of Surveillance Technology Companies on Policing; In Law Review Online, New York University (NYU); 2017-09; N pages; landing.
Elizabeth E. Joh is Professor of Law, School of Law, U.C. Davis.


Conventional wisdom assumes that the police are in control of their investigative tools. But with surveillance technologies, this is not always the case. Increasingly, police departments are consumers of surveillance technologies that are created, sold, and controlled by private companies. These surveillance technology companies exercise an undue influence over the police today in ways that aren’t widely acknowledged, but that have enormous consequences for civil liberties and police oversight. Three seemingly unrelated examples—stingray cellphone surveillance, body cameras, and big data software—demonstrate varieties of this undue influence. The companies which provide these technologies act out of private self-interest, but their decisions have considerable public impact. The harms of this private influence include the distortion of Fourth Amendment law, the undermining of accountability by design, and the erosion of transparency norms. This Essay demonstrates the increasing degree to which surveillance technology vendors can guide, shape, and limit policing in ways that are not widely recognized. Any vision of increased police accountability today cannot be complete without consideration of the role surveillance technology companies play.


    1. Stingray Cellphone Surveillance and Nondisclosure Agreements
      1. Nondisclosure Agreements
      2. Stingrays and the Fourth Amendment
      3. Secret Stingray Use
    2. Cornering the Market on Police Body Cameras
      1. When Product Design Is Policy
      2. Market Dominance
    3. Big Data Software and Proprietary Information
    1. Fourth Amendment Distortion
    2. Accountability by Design
    3. Outsourcing Suspicion and Obscuring Transparency
    1. Local Surveillance Oversight
    2. Public Records Requests as Oversight

Toward a Fourth Law of Robotics: Preserving Attribution, Responsibility, and Explainability in an Algorithmic Society | Pasquale

Frank A. Pasquale III; Toward a Fourth Law of Robotics: Preserving Attribution, Responsibility, and Explainability in an Algorithmic Society; Ohio State Law Journal, Vol. 78, 2017, U of Maryland Legal Studies Research Paper No. 2017-21; 2017-07-14; 13 pages; ssrn:3002546.

tl;dr → A comment for Balkin. To wit:
  1. Balkin should have supplied more context; such correction is supplied herewith.
  2. More expansive supervision is indicated; such expansion is supplied herewith.
  3. Another law is warranted; not a trinity, but perfection plus one more

Four Laws, here and previous:

  1. machine operators are always responsible for their machines.
  2. businesses are always responsible for their operators.
  3. machines must not pollute.
  4. A [machine] must always indicate the identity of its creator, controller, or owner.

Love the erudition; but this is just like planes, trains & automobiles.

Separately noted.

The Three Laws of Robotics in the Age of Big Data | Balkin

Jack M. Balkin  (Yale); The Three Laws of Robotics in the Age of Big Data; Ohio State Law Journal, Vol. 78, (2017), Forthcoming (real soon now, RSN), Yale Law School, Public Law Research Paper No. 592; 2016-12-29 → 2017-09-10; 45 pages; ssrn:2890965.

tl;dr → administrative laws [should be] directed at human beings and human organizations, not at [machines].


  1. machine operators are responsible
    [for the operations of their machines, always & everywhere]
  2. businesses are responsible
    [for the operation of their machines, always & everywhere]
  3. machines must not pollute
    [in a sense to be defined later: e.g. by a "tussle"]

Love the erudition; but none of this is new.

Separately noted.

Big Data Surveillance: The Case of Policing | Sarah Brayne


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.


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.


  • 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)


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


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.



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Incompatible: The GDPR in the Age of Big Data | Tal Zarsky

Tal Zarsky (Haifa); Incompatible: The GDPR in the Age of Big Data; Seton Hall Law Review, Vol. 47, No. 4(2), 2017; 2017-08-22; 26 pages; ssrn:3022646.

tl;dr → the opposition is elucidated and juxtaposed; the domain is problematized.
and → “Big Data,” by definition, is opportunistic and unsupervisable; it collects everything and identifies something later in the backend.  Else it is not “Big Data” (it is “little data,” which is known, familiar, boring, and of course has settled law surrounding its operational envelope).


After years of drafting and negotiations, the EU finally passed the General Data Protection Regulation (GDPR). The GDPR’s impact will, most likely, be profound. Among the challenges data protection law faces in the digital age, the emergence of Big Data is perhaps the greatest. Indeed, Big Data analysis carries both hope and potential harm to the individuals whose data is analyzed, as well as other individuals indirectly affected by such analyses. These novel developments call for both conceptual and practical changes in the current legal setting.

Unfortunately, the GDPR fails to properly address the surge in Big Data practices. The GDPR’s provisions are — to borrow a key term used throughout EU data protection regulation — incompatible with the data environment that the availability of Big Data generates. Such incompatibility is destined to render many of the GDPR’s provisions quickly irrelevant. Alternatively, the GDPR’s enactment could substantially alter the way Big Data analysis is conducted, transferring it to one that is suboptimal and inefficient. It will do so while stalling innovation in Europe and limiting utility to European citizens, while not necessarily providing such citizens with greater privacy protection.

After a brief introduction (Part I), Part II quickly defines Big Data and its relevance to EU data protection law. Part III addresses four central concepts of EU data protection law as manifested in the GDPR: Purpose Specification, Data Minimization, Automated Decisions and Special Categories. It thereafter proceeds to demonstrate that the treatment of every one of these concepts in the GDPR is lacking and in fact incompatible with the prospects of Big Data analysis. Part IV concludes by discussing the aggregated effect of such incompatibilities on regulated entities, the EU, and society in general.


<snide>Apparently this was not known before the activists captured the legislature and affected their ends with the force of law. Now we know. Yet we all must obey the law, as it stands and as it is written. And why was this not published in an EU-located law journal, perhaps one located in … Brussels?</snide>


A. Purpose Limitation
B. Data Minimization
C. Special Categories
D. Automated Decisions


There are 123 references, manifested as footnotes in the legal style.

Separately noted.

The Spread of Mass Surveillance, 1995 to Present (Big Data Innovation Transfer and Governance in Emerging High Technology States) | CPS

Nadiya Kostyuk, Muzammil M. Hussain (CPD); The Spread of Mass Surveillance, 1995 to Present; In Their Blog at the Center for Political Studies (CPS), Institute for Social Research, University of Michigan; 2017-09-01.
Previously performed at the 2017 Annual Meeting of the American Political Science Association (APSA); the presentation, titled “Big Data Innovation Transfer and Governance in Emerging High Technology States” was a part of the session “The Role of Business in Information Technology and Politics” on Friday 2017-09-01.

tl;dr → an exercise in documentation; factoids are developed; a diversity is shown.
<quote>The observed cases in our study differ in scope and impact.</quote>

Original Sources


  • Aadhaar, a national ID program, India.
  • Social Credit System, China.


Categorical (arbitrary) Total Spend (USD) Spend/Individual (USD) Span (count) Coverage Universe Fun
nations worldwide $27.1B
(or more)
$7 4.138B 73% world population
stable autocracies,
authoritarian regimes
$10.967B $lower-$110 0.1B 81% their populations upper is 11X more
than “other regime type”
advanced democracies $8.909B $11 0.812B 74% their population
high-spending dictatorships and democracies,
developing and emerging democracies
$4.784B $1-2 2.875B 72% their population


AI and ‘Enormous Data’ Could Make Tech Giants Harder to Topple | Wired

AI and ‘Enormous Data’ Could Make Tech Giants Harder to Topple; ; In Wired; 2017-07-13.

tl;dr → <quote>such releases don’t usually offer much of value to potential competitors. </quote> They are promotional and self-serving.



  • TensorFlow
  • Common Visual Data Foundation
    • open image data sets
    • A “nonprofit”
    • Sponsors
      • Facebook
      • Microsoft
  • Other data sets
    • from YouTube, by Google
    • from Wikipedia, by Salesforce


  • Google
  • Microsoft
  • and others!
    • Salesforce
    • Uber
  • Manifold, a boutique
  •, a boutique


  • Luke de Oliveira
    • partner, Manifold
    • temp staff, visitor, Lawrence Berkeley National Lab
  • Abhinav Gupta, Carnegie Mellon University (CMU)
  • Rachel Thomas, cofounder,


Enormous Data
Are you kidding me? Do you even use computers?
incumbents’ usual data advantage
innovative and un-monopolistic by disruption
Appears in the 1st paragraph


The Wikitext Long Term Dependency Language Modeling Dataset; On Some Site

  • an announcement, but WHEN?


In Wired


The Princeton Web Transparency And Accountability Project | Narayanan, Reisman

Arvind Narayanan, Dillon Reisman; The Princeton Web Transparency and Accountability Project; In Tania Cerquitelli, Daniele Quercia, Frank Pasquale (editors); Transparent Data Mining for Big and Small Data; Springer; 2017.

tl;dr → There be dragons. Princeton was is there. Tell it! Testify!


When you browse the web, hidden “third parties” collect a large amount of data about your behavior. This data feeds algorithms to target ads to you, tailor your news recommendations, and sometimes vary prices of online products. The network of trackers comprises hundreds of entities, but consumers have little awareness of its pervasiveness and sophistication. This chapter discusses the findings and experiences of the Princeton Web Transparency Project, which continually monitors the web to uncover what user data companies collect, how they collect it, and what they do with it. We do this via a largely automated monthly “census” of the top 1 million websites, in effect “tracking the trackers”. Our tools and findings have proven useful to regulators and investigatory journalists, and have led to greater public awareness, the cessation of some privacy-infringing practices, and the creation of new consumer privacy tools. But the work raises many new questions. For example, should we hold websites accountable for the privacy breaches caused by third parties? The chapter concludes with a discussion of such tricky issues and makes recommendations for public policy and regulation of privacy.


  • Marvin Minsky
  • expert systems
  • Machine Learning
  • Artifical Intelligence
  • Big Data
  • Netflix
  • Self-Driving Cars
  • collect data first, ask questions later
  • surveillance infrastructure
  • Kafkaesque
  • data and algorithmic transparency
  • Workshop on Data and Algorithmic Transparency
  • Princeton Web Transparency and Accountability Project (WebTAP)
    Princeton Web Census
  • Privacy scholar
  • Ryan Calo
  • The Party System
  • first party
  • third party
  • Twitter
  • Facebook
  • Facebook Like Button
  • The Beauty and the Beast Project
  • Panopticlick
  • Anonymous
  • Pseudonymous
  • biases
  • discrimination
  • targeted political messaging
  • price discrimination
  • market manipulation
  • AdChoices
  • ad blockers
  • Federal Trade Commission (FTC)
  • Optimizely
  • A/B Testing
  • OpenWPM (Open Web Privacy Measurement)
  • FourthParth
  • FPDetective
  • PhamtomJS
  • Firefox
  • Tor
  • Facebook Connect
  • Google Single Sign-On (SSO)
  • longitudinal studies
  • HTML5, Canvas API
  • canvas fingerprinting
  • AddThis
  • AudioContext API
  • WebRTC API
  • Battery Status API
  • NSA (National Security Agency)
  • Snowden
  • Cookies
  • transitive cookie linking
  • cookie syncing
  • Google
  • Facebook
  • Federal Trade Commission (FTC)
  • Cross-Device Tracking
  • header enrichment (by ISPs)
  • Ghostery
  • AdBlock Plus
  • uBlock Origin
  • machine learning classifier (for tracking behavior)
  • Big Data (they used Big Data and Machine Learning Classifiers)
  • Nudge (a book)
  • Choice Architecture
  • 3rd Part Cookies, blocking 3rd party cookies
  • Do Not Track
  • Battery API
  • Internet Explorer
  • zero sum game
  • power user interfaces
  • PGP (Pretty Good Privacy)
  • Cookie Blocking
  • <buzz>long tail (of innovation)</buzz>
  • Children’s Online Privacy Protection Act (COPPA)
  • child-directed websites.
  • American Civil Liberties Union (ACLU)
  • Computer Fraud and Abuse Act
  • Personally-Identifiable Information (PII)
  • shift of power, from 3rd parties to publishers
  • Columbia University
  • Carnegie Mellon University
  • Internet of Things (IoT)
  • WiFi
  • cross-device tracking
  • smartphone app
  • Fairness, Accountability and Transparency in Machine Learning (FAT-ML)
  • Princeton


  • “The best minds of our generation are thinking about how to make people click on ads” attributed to Jeff Hammerbacher


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How to Call B.S. on Big Data: A Practical Guide | New Yorker

How to Call B.S. on Big Data: A Practical Guide; ; In The New Yorker/ 2017-06-03.


INFO 198/BIOL 106B ( – “Calling Bullshit in the Age of Big Data,” a course, University of Washington (Washington State, that is, located in Seattle WA). Instructors:  Jevin West (iinformation), Carl Bergstrom (biology)




In The New  Yorker

The Death of Rules and Standards | Casey, Niblett

Anthony J. Casey, Anthony Niblett; The Death of Rules and Standards; Coase-Sandor Working Paper Series in Law and Economics No. 738; Law School, University of Chicago; 2015; 58 pages; landing, copy, ssrn:2693826, draft.


Scholars have examined the lawmakers’ choice between rules and standards for decades. This paper, however, explores the possibility of a new form of law that renders that choice unnecessary. Advances in technology (such as big data and artificial intelligence) will give rise to this new form – the micro-directive – which will provide the benefits of both rules and standards without the costs of either.

Lawmakers will be able to use predictive and communication technologies to enact complex legislative goals that are translated by machines into a vast catalog of simple commands for all possible scenarios. When an individual citizen faces a legal choice, the machine will select from the catalog and communicate to that individual the precise context-specific command (the micro-directive) necessary for compliance. In this way, law will be able to adapt to a wide array of situations and direct precise citizen behavior without further legislative or judicial action. A micro-directive, like a rule, provides a clear instruction to a citizen on how to comply with the law. But, like a standard, a micro-directive is tailored to and adapts to each and every context.

While predictive technologies such as big data have already introduced a trend toward personalized default rules, in this paper we suggest that this is only a small part of a larger trend toward context- specific laws that can adapt to any situation. As that trend continues, the fundamental cost trade-off between rules and standards will disappear, changing the way society structures and thinks about law.

Separately noted.