Category: Surveillance


The Flawed Foundations of Article III Standing in Surveillance Cases (Part IV)

In my first three posts, I’ve opened a critical discussion of Article III standing for plaintiffs challenging government surveillance programs by introducing the 1972 Supreme Court case of Laird v. Tatum. In today’s post, I’ll examine the Court’s decision itself, which held that chilling effects arising “merely from the individual’s knowledge” of likely government surveillance did not constitute adequate injury to meet Article III standing requirements.

The Burger Court

It didn’t take long for courts to embrace Laird as a useful tool to dismiss cases where plaintiffs sought to challenge government surveillance programs, especially where the complaints rested on a First Amendment chill from political profiling by law enforcement. Some judges took exception to a broad interpretation of Laird, but objections largely showed up in dissenting opinions. For the most part, early interpretations of Laird sympathized with the government’s view of surveillance claims.

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The Flawed Foundations of Article III Standing in Surveillance Cases (Part III)

In my first two posts, I’ve opened a critical discussion of Article III standing for plaintiffs challenging government surveillance programs by introducing the 1972 Supreme Court case of Laird v. Tatum. In today’s post, I’ll examine the Court’s decision itself, which held that chilling effects arising “merely from the individual’s knowledge” of likely government surveillance did not constitute adequate injury to meet Article III standing requirements.

Then-Secretary of Defense Melvin Laird Sharing a Light Moment With President Nixon

Then-Secretary of Defense Melvin Laird Sharing a Light Moment With President Nixon

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The Flawed Foundations of Article III Standing in Surveillance Cases (Part I)

I’m grateful for the opportunity to be a Concurring Opinions guest blogger this month. My posts will largely concentrate on the history of Article III standing for plaintiffs seeking to challenge government surveillance programs, and the flawed foundations upon which our federal standing jurisprudence rests. 


Then-Secretary of Defense Melvin Laird Sharing a Light Moment With President Nixon

Then-Secretary of Defense Melvin Laird Sharing a Light Moment With President Nixon (Wikimedia Commons)

Plaintiffs seeking to challenge government surveillance programs have faced long odds in federal courts, due mainly to a line of Supreme Court cases that have set a very high bar to Article III standing in these cases. The origins of this jurisprudence can be directly traced to Laird v. Tatum, a 1972 case where the Supreme Court considered the question of who could sue the government over a surveillance program, holding in a 5-4 decision that chilling effects arising “merely from the individual’s knowledge” of likely government surveillance did not constitute adequate injury to meet Article III standing requirements. Federal courts have since relied upon Laird to deny standing to plaintiffs in surveillance cases, including the 2013 Supreme Court decision in Clapper v. Amnesty Int’l USA. But the facts behind Laird illuminate a number of important reasons why it is a weak basis for surveillance standing doctrine. It is therefore a worthwhile endeavor, I think, to reexamine Laird in a post-Snowden context in order to gain a deeper understanding of the Court’s flawed standing doctrine in surveillance cases.

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Interview on The Black Box Society

BBSBalkinization just published an interview on my forthcoming book, The Black Box Society. Law profs may be interested in our dialogue on methodology—particularly, what the unique role of the legal scholar is in the midst of increasing academic specialization. I’ve tried to surface several strands of inspiration for the book.

Social Science in an Era of Corporate Big Data

IsaacWorkingIn my last post, I explored the characteristics of Facebook’s model (i.e., exemplary) users. Today, I want to discuss the model users in the company–i.e., the data scientists who try to build stylized versions of reality (models) based on certain data points and theories. The Facebook emotion experiment is part of a much larger reshaping of social science. To what extent will academics study data driven firms like Facebook, and to what extent will they try to join forces with its own researchers to study others?

Present incentives are clear: collaborate with (rather than develop a critical theory of) big data firms.  As Zeynep Tufekci puts it, “the most valuable datasets have become corporate and proprietary [and] top journals love publishing from them.”  “Big data” has an aura of scientific validity simply because of the velocity, volume, and variety of the phenomena it encompasses. Psychologists certainly must have learned *something* from looking at over 600,000 accounts’ activity, right?

The problem, though is that the corporate “science” of manipulation is a far cry from academic science’s ethics of openness and reproducibility.* That’s already led to some embarrassments in the crossover from corporate to academic modeling (such as Google’s flu trends failures). Researchers within Facebook worried about multiple experiments being performed at once on individual users, which might compromise the results of any one study. Standardized review could have prevented that. But, true to the Silicon Valley ethic of “move fast and break things,” speed was paramount: “There’s no review process. Anyone…could run a test…trying to alter peoples’ behavior,” said one former Facebook data scientist.

Grant Getters and Committee Men

Why are journals so interested in this form of research? Why are academics jumping on board? Fortunately, social science has matured to the point that we now have a robust, insightful literature about the nature of social science itself. I know, this probably sounds awfully meta–exactly the type of navel-gazing Senator Coburn would excommunicate from the church of science. But it actually provides a much-needed historical perspective on how power and money shape knowledge. Consider, for instance, the opening of Joel Isaac’s article Tangled Loops, on Cold War social science:

During the first two decades of the Cold War, a new kind of academic figure became prominent in American public life: the credentialed social scientist or expert in the sciences of administration who was also, to use the parlance of the time, a “man of affairs.” Some were academic high-fliers conscripted into government roles in which their intellectual and organizational talents could be exploited. McGeorge Bundy, Walt Rostow, and Robert McNamara are the archetypes of such persons. An overlapping group of scholars became policymakers and political advisers on issues ranging from social welfare provision to nation-building in emerging postcolonial states.

Postwar leaders of the social and administrative sciences such as Talcott Parsons and Herbert Simon were skilled scientific brokers of just this sort: good “committee men,” grant-getters, proponents of interdisciplinary inquiry, and institution-builders. This hard-nosed, suit-wearing, business-like persona was connected to new, technologically refined forms of social science. . . . Antediluvian “social science” was eschewed in favour of mathematical, behavioural, and systems-based approaches to “human relations” such as operations research, behavioral science, game theory, systems theory, and cognitive science.

One of Isaac’s major contributions in that piece is to interpret the social science coming out of the academy (and entities like RAND) as a cultural practice: “Insofar as theories involve certain forms of practice, they are caught up in worldly, quotidian matters: performances, comportments, training regimes, and so on.” Government leveraged funding to mobilize research to specific ends. To maintain university patronage systems and research centers, leaders had to be on good terms with the grantors. The common goal of strengthening the US economy (and defeating the communist threat) cemented an ideological alliance.

Government still exerts influence in American social and behavioral sciences. But private industry controls critical data sets for the most glamorous, data-driven research. In the Cold War era, “grant getting” may have been the key to economic security, and to securing one’s voice in the university. Today, “exit” options are more important than voice, and what better place to exit to than an internet platform? Thus academic/corporate “flexians” shuttle between the two worlds. Their research cannot be too venal, lest the academy disdain it. But neither can it indulge in, say, critical theory (what would nonprofit social networks look like), just as Cold War social scientists were ill-advised to, say, develop Myrdal’s or Leontief’s theories. There was a lot more money available for the Friedmanite direction economics would, eventually, take.

Intensifying academic precarity also makes the blandishments of corporate data science an “offer one can’t refuse.” Tenured jobs are growing scarcer. As MOOCmongers aspire to deskill and commoditize the academy, industry’s benefits and flexibility grow ever more alluring. Academic IRBs can impose a heavy bureaucratic burden; the corporate world is far more flexible. (Consider all the defenses of the Facebook authored last week which emphasized how little review corporate research has to go through: satisfy the boss, and you’re basically done, no matter how troubling your aims or methods may be in a purely academic context.)

Creating Kinds

So why does all this matter, other than to the quantitatively gifted individuals at the cutting edge of data science? It matters because, in Isaac’s words:

Theories and classifications in the human sciences do not “discover” an independently existing reality; they help, in part, to create it. Much of this comes down to the publicity of knowledge. Insofar as scientific descriptions of people are made available to the public, they may “change how we can think of ourselves, [and] change our sense of self-worth, even how we remember our own past.

It is very hard to develop categories and kinds for internet firms, because they are so secretive about most of their operations. (And make no mistake about the current PR kerfuffle for Facebook: it will lead the company to become ever more secretive about its data science, just as Target started camouflaging its pregnancy-related ads and not talking to reporters after people appeared creeped out by the uncanny accuracy of its natal predictions.) But the data collection of the firms is creating whole new kinds of people—for marketers, for the NSA, and for anyone with the money or connections to access the information.

More likely than not, encoded in Facebook’s database is some new, milder DSM, with categories like the slightly stingy (who need to be induced to buy more); the profligate, who need frugality prompts; the creepy, who need to be hidden in newsfeeds lest they bum out the cool. Our new “Science Mart” creates these new human kinds, but also alters them, as “new sorting and theorizing induces changes in self-conception and in behavior of the people classified.” Perhaps in the future, upon being classified as “slightly depressed” by Facebook, users will see more happy posts. Perhaps the hypomanic will be brought down a bit. Or, perhaps if their state is better for business, it will be cultivated and promoted.

You may think that last possibility unfair, or a mischaracterization of the power of Facebook. But shouldn’t children have been excluded from its emotion experiment? Shouldn’t those whom it suspects may be clinically depressed? Shouldn’t some independent reviewer have asked about those possibilities? Journalists try to reassure us that Facebook is better now than it was 2 years ago. But the power imbalances in social science remain as funding cuts threaten researchers’ autonomy. Until research in general is properly valued, we can expect more psychologists, anthropologists, and data scientists to attune themselves to corporate research agendas, rather than questioning why data about users is so much more available than data about company practices.

Image Note: I’ve inserted a picture of Isaac’s book, which I highly recommend to readers interested in the history of social science.

*I suggested this was a problem in 2010.

Facebook’s Model Users

DontAnthropomorphizePeopleFacebook’s recent pscyhology experiment has raised difficult questions about the ethical standards of data-driven companies, and the universities that collaborate with them. We are still learning exactly who did what before publication. Some are wisely calling for a “People’s Terms of Service” agreement to curb further abuses. Others are more focused on the responsibility to protect research subjects. As Jack Balkin has suggested, we need these massive internet platforms to act as fiduciaries.

The experiment fiasco is just the latest in a long history of ethically troubling decisions at that firm, and several others like it. And the time is long past for serious, international action to impose some basic ethical limits on the business practices these behemoths pursue.

Unfortunately, many in Silicon Valley still barely get what the fuss is about. For them, A/B testing is simply a way of life. Using it to make people feel better or worse is a far cry from, say, manipulating video poker machines to squeeze a few extra dollars out of desperate consumers. “Casino owners do that all the time!”, one can almost hear them rejoin.

Yet there are some revealing similarities between casinos and major internet platforms. Consider this analogy from Rob Horning:

Social media platforms are engineered to be sticky — that is, addictive, as Alexis Madrigal details in [a] post about the “machine zone.” . . . Like video slots, which incite extended periods of “time-on-machine” to assure “continuous gaming productivity” (i.e. money extraction from players), social-media sites are designed to maximize time-on-site, to make their users more valuable to advertisers (Instagram, incidentally, is adding advertising) and to ratchet up user productivity in the form of data sharing and processing that social-media sites reserve the rights to.

That’s one reason we get headlines like “Teens Can’t Stop Using Facebook Even Though They Hate It.” There are sociobiological routes to conditioning action. The platforms are constantly shaping us, based on sophisticated psychological profiles.

For Facebook to continue to meet Wall Street’s demands for growth, its user base must grow and/or individual users must become more “productive.” Predictive analytics demands standardization: forecastable estimates of revenue-per-user. The more a person clicks on ads and buys products, the better. Secondarily, the more a person draws other potential ad-clickers in–via clicked-on content, catalyzing discussions, crying for help, whatever–the more valuable they become to the platform. The “model users” gain visibility, subtly instructing by example how to act on the network. They’ll probably never attain the notoriety of a Lei Feng, but the Republic of Facebookistan gladly pays them the currency of attention, as long as the investment pays off for top managers and shareholders.

As more people understand the implications of enjoying Facebook “for free“–i.e., that they are the product of the service–they also see that its real paying customers are advertisers. As Katherine Hayles has stated, the critical question here is: “will ubiquitous computing be coopted as a stalking horse for predatory capitalism, or can we seize the opportunity” to deploy more emancipatory uses of it?  I have expressed faith in the latter possibility, but Facebook continually validates Julie Cohen’s critique of a surveillance-innovation complex.

Facebook’s Hidden Persuaders

hidden-persuadersMajor internet platforms are constantly trying new things out on users, to better change their interfaces. Perhaps they’re interested in changing their users, too. Consider this account of Facebook’s manipulation of its newsfeed:

If you were feeling glum in January 2012, it might not have been you. Facebook ran an experiment on 689,003 users to see if it could manipulate their emotions. One experimental group had stories with positive words like “love” and “nice” filtered out of their News Feeds; another experimental group had stories with negative words like “hurt” and “nasty” filtered out. And indeed, people who saw fewer positive posts created fewer of their own. Facebook made them sad for a psych experiment.

James Grimmelmann suggests some potential legal and ethical pitfalls. Julie Cohen has dissected the larger political economy of modulation. For now, I’d just like to present a subtle shift in Silicon Valley rhetoric:

c. 2008: “How dare you suggest we’d manipulate our users! What a paranoid view.”
c. 2014: “Of course we manipulate users! That’s how we optimize time-on-machine.”

There are many cards in the denialists’ deck. An earlier Facebook-inspired study warns of “greater spikes in global emotion that could generate increased volatility in everything from political systems to financial markets.” Perhaps social networks will take on the dampening of inconvenient emotions as a public service. For a few glimpses of the road ahead, take a look at Bernard Harcourt (on Zunzuneo), Jonathan Zittrain, Robert Epstein, and N. Katherine Hayles.

Endless Replay, Continued

My post on surveillance creating an “endless replay” has some more playful applications, as well. Consider the “Groundhog Date” now marketed by “Partnering with an LA based company … that matches people to dates using facial recognition software, users will be asked to send in pictures of their exes, which will be used to determine who they will be matched with on the site.” Critics are aghast at the prospect of outsourcing our humanity. However habitual our actions may be, no one wants to be typecast as a typecaster.