September 20: Madelyn Rose Sanfilippo AND Yafit Lev-Aretz — Breaking News: How Push Notifications Alter the Fourth Estate
ABSTRACT: News outlets increasingly capitalize on the potential of push notifications to drive engagement and enhance readership. Such changes in news reporting and consumption offer a new, largely overlooked, research perspective into the competing narratives about the definition of news, their impact on political participation, entrenchment of political views, the ubiquity of media environments, and anxiety in media consumption. Situated within discussions about fake news, how new technologies have changed journalism, and the nature of news consumption overall, this paper and a larger ongoing empirical project seek to explore: 1) how push notifications and online “breaking news” phenomenon differ from traditional news reporting; 2) relationships between objectivity in journalism, reader affect and trust; and 3) what this means for participatory politics and its relationship to the fourth estate. This article illustrates patterns and key insights about the impact of push notifications on journalism and changes in sentiment in news communication through a case study comparing reporting on President Nixon firing Special Prosecutor Archibald Cox in 1973 to the recent firing of FBI Director James Comey by President Trump. While headlines and push notifications vary significantly by news providers, push notifications are similar across platforms in distinguishing characteristics such as emotionally-loaded and subjective language. Both of these are defining elements of fake and deceptive news and may potentially account for some of the media mistrust in recent years.
September 13: Ignacio Cofone — Anti-Discriminatory Privacy
ABSTRACT: The paper examines the information dynamics of privacy and discrimination (Strahilevitz 2007, Roberts 2015) to design anti-discriminatory privacy rules, especially for statistical and algorithmic discrimination (Barocas and Selbst 2016, Kim 2017). To do so, it uses empirical studies of informational anti-discriminatory rules (Goldin and Rouse 1997, Agan and Starr 2016) and explores how privacy rules can overcome the limitations that these rules faced. It proposes that taste-based discrimination and statistical discrimination, a traditional distinction in economics, have the same information dynamic and should therefore be addressed similarly by privacy law. The common element between different kinds of discrimination is that, to effectively prevent them, informational rules must focus on blocking information flows that can be used to shift discrimination to other groups (e.g. former inmates versus black men). Anti-discriminatory privacy rules, in other words, should block not only undesirable information but also their proxies. The paper develops a theory on how to identify such proxies based on the cross-elasticity of information. It then applies this idea to algorithmic discrimination and proposes that the literature has so far brought legal solutions to an information problem. The paper proposes an information solution to the informational problem instead.
April 26: Ben Zevenbergen — Contextual Integrity as a Framework for Internet Research Ethics
ABSTRACT: This doctoral work investigates to what extent the theory of Contextual Integrity can be used (or enhanced) to inform an ethics review procedure for Internet research. The project uses a structured case methodology, which can be used to test and enhance a theory. The analytical framework from literature is the starting point in this methodology, which consist of internet research ethics methodologies, contextual integrity, and the principles of purpose limitation and data minimization. The thesis then assesses three cases through this lens that increase in technical complexity, whereby the findings of one case feed into the analysis of the next. After the three case studies have been completed, the thesis concludes with a chapter about how the analytical framework developed throughout the thesis, where the methodology succeeds, and where there may be issues that will need to be addressed. The cases to be addressed are projects from 1) Internet measurement, 2) data and algorithmic transparency, and 3) artificial intelligence. Please note, Ben has only recently changed the focus of his research from privacy engineering to Internet research ethics. His former supervisor had to leave academia, so he merged his ongoing side project on research ethics with the methodology and analytical lens of his PhD thesis. The thesis is thus very much an ongoing work. Please have a look at this guideline document, which has been the result of Ben’s side project and is very much informed/inspired by Contextual Integrity: http://networkedsystemsethics.net/
April 19: Beate Roessler — Manipulation
ABSTRACT: The problem we want to discuss is what precisely it is in techniques like behavioural targeting that is worrying. These techniques seem to influence our behavior, our actions in certain ways: and we want to get at the reasons why these ways could be illegitimate and harmful. What we suspect is that it is a form of manipulation which makes these techniques harmful; therefore, we are going to unwrap the concept of manipulation, try to make conceptual distinctions which can be linked back to the cases, and make some suggestions what the harm of manipulation consists in conflicts.
April 12: Amanda Levendowski — Conflict Modeling
ABSTRACT: Conflict modeling offers a methodology rooted in case studies to identify and prioritize online conflicts and think about ways to mitigate the risks of those conflicts. Online systems—from social media platforms like Facebook and Twitter, to communities like reddit, to online games like League of Legends—are rife with conflict, and are notoriously bad at dealing with it. Abuse, clashes, and tensions (broadly "conflict") can arise between users or between users and the system itself, and online systems too often respond to conflict with ad hoc riffs on the Politician's Fallacy: We have a problem, we must do something, this is something, so we must do this. Except that “this” can end up causing other types of conflict. Conflict modeling adapts security threat modeling into a similarly systemic and predictable approach for spotting conflict. Conflict modeling draws from computer science literature related to threat modeling and value-sensitive design and builds on the legal literature regarding adapting threat modeling to privacy problems to offer a taxonomy of the kinds of conflicts that can arise on a system—broadly, safety, comfort, usability, legal, privacy, and transparency conflicts—as well as known techniques for mitigating those conflicts.
April 5: Madelyn Sanfilippo — Privacy as Commons: A Conceptual Overview and Case Study in Progress
ABSTRACT: Conceptualizing privacy in terms of information flows within a knowledge commons augments Helen Nissenbaum’s “privacy as contextual integrity” approach. Nissenbaum’s framework focuses on “appropriate flow of personal information”, as determined by contextual norms. The Governing Knowledge Commons (GKC) framework, which builds on Ostrom’s Institutional Analysis and Development (IAD) framework, highlights the development and sharing of knowledge resources among community members according to rules-in-use. Comparing Nissenbaum’s framework with the privacy commons approach highlights the reciprocal relationships between constraint and control over personal information and openness and sharing. For example, a group might make a collective decision to deploy the Chatham House Rules, constraining information flows to outsiders as a means of encouraging greater sharing among members. By viewing privacy as information flow rules-in-use constructed within a specific commons arrangement, the GKC framework goes beyond recognizing the importance of existing norms of appropriate information flow, drawing attention to the formal and informal governance mechanisms by which rules-in-use for information flows are created and maintained and providing tools for analyzing those mechanisms. This work also builds on multifaceted conceptualizations of privacy, such as those articulated by Solove and Bennett. This presentation will provide an overview of a series of projects addressing commons governance of privacy, including conceptual work and meta-analysis of privacy issues in knowledge commons cases with Katherine Strandburg and Brett Frischmann, as well as a case study, in progress, of policy networks as privacy commons. Following a theoretical explication of the GKC privacy commons framework, the Chatham House Rules example and the on-gong case study will be discussed.
March 29: Hugo Zylberberg — Reframing the fake news debate: influence operations, targeting-and-convincing infrastructure and exploitation of personal data
ABSTRACT: Privacy advocates and the national security community have long been at odds with each other. Starting with the first Crypto Wars, the framing of encryption issues as a tradeoff between privacy and security (i.e. if you want more security, you will have to give some of your privacy away) in the digital world has offered these two community a zero-sum game to play, as in the public debate around backdoors. But in a world where privately-owned targeting-and-convincing infrastructures can be leased by organizations to efficiently influence people’s decision-making processes, privacy and security are no longer opposed - rather, they are two sides of the same coin. In this article, we describe the privacy-security tradeoff and explain how it evolves in the era of surveillance capitalism and targeting-and-convincing infrastructures. Crucially, these infrastructure rely on the collection of personal data on a massive scale, which enables us to make a security argument for data protection.
March 22: Caroline Alewaerts, Eli Siems and Nate Tisa will lead discussion of three topics flagged during our current events roundups: smart toys, the recently leaked documents about CIA surveillance techniques, and the issues raised by the government’s attempt to obtain recordings from an Amazon Echo in a criminal trial. Background reading:
1. Smart Toys:
- An article describing the data breach that recently affected the smart toy "CloudPets": https://www.forbes.com/sites/leemathews/2017/02/28/cloudpets-data-leak-is-a-privacy-nightmare-for-parents-and-kids/#6179cc4ab0bf
- An article describing the existing tension between the aspirations of the toy manufacturing industry and the requirements of the children privacy legislation: https://www.cnet.com/news/smart-toys-connected-internet-of-things-voice-recording-coppa-children-privacy-parents-kids/
- Report from Senate Bill Nelson on privacy and security concerns relating to smart toys: https://www.billnelson.senate.gov/sites/default/files/12.14.16_Ranking_Member_Nelson_Report_on_Connected_Toys.pdf
2. CIA Surveillance Techniques Leak:
- Background article from the Guardian: https://www.theguardian.com/media/2017/mar/07/wikileaks-publishes-biggest-ever-leak-of-secret-cia-documents-hacking-surveillance
- Update on Wikileaks' efforts to share the zero day exploits with tech companies: https://techcrunch.com/2017/03/17/wikileaks-tech-companies-demands/
- Tech companies’ recent responses to the leaks: https://www.benzinga.com/news/17/03/9188862/intel-others-respond-to-vault-7-cia-wikileaks-with-new-security-tools
3. Amazon Echo Recordings:
- Discussion of Amazon’s First Amendment argument against producing the recordings: https://www.forbes.com/sites/thomasbrewster/2017/02/23/amazon-echo-alexa-murder-trial-first-amendment-rights/#59f5ad145d81
- Report on Amazon dropping the argument and releasing the data http://www.pbs.org/newshour/rundown/amazon-releases-echo-data-murder-case-dropping-first-amendment-argument/
March 8: Ira Rubinstein — Privacy Localism
ABSTRACT: This is an early-stage presentation of an article in which I hope to offer the first in-depth study of what I will call “privacy localism.” Using case studies of three activist cities, Seattle, Oakland, and New York City, I will examine the origins, motivations, and outcomes of city-based efforts to develop privacy principles and practices while providing city services, pursuing smart city and open data initiatives, and carrying out both local police and counterterrorism activities. Cities are data rich environments for obvious reasons: large populations that generate a vast array of data through their use of city services, their encounters with local police, and their daily interaction with a variety of widely-deployed surveillance technologies such as license plate readers, police dashboard and body cameras, and gunfire location services. “Smart cities” collect even more data and local police forces draw on all these data sources for crime prevention and criminal investigation purposes. In the past few years, cities like Seattle and Oakland have begun to engage in privacy localism, launching privacy initiatives defining how they collect, use, and dispose of data and imposing citywide requirements on the funding, acquisition, and use of surveillance technologies. For example, Seattle has adopted an ordinance relating to its use of surveillance equipment and requiring City departments (1) to obtain City Council approval prior to acquiring certain surveillance equipment and (2) to propose protocols related to proper use and deployment of such equipment and addressing data retention, storage and access of any data obtained through its use. Oakland is taking similar steps as is NYC (as of just last week). How did these developments come about? What is their scope and likelihood of success given that cities have a very weak hand to play in the face of (1) the competing needs and interests of their own local police forces; (2) their limited powers under state constitutions and statutes, which make them almost entirely subject to state control; (3) their reliance on federal grants from the DHS or the DOJ, which make purchases of new police technologies subject to a variety of federal data sharing and other requirements, and which may violate local privacy principles or surveillance ordinances; and (4) the high likelihood of federal and state privacy laws preempting local rulemaking initiatives? This paper makes four claims: First, that cities are salient to privacy debates for at least six reasons, which I discuss under the following headings: Localism, urbanization, urban tech, public spaces, local police surveillance, and stalemate at the federal level. Second, that cities have limited but sufficient power to protect local citizens’ privacy. This section draws on the federalism literature to develop a theoretical framework in which cities occupy a discretionary space in which they may engage in three main privacy-related activities: smart city self-governance; regulation of local police surveillance; and resistance to federal laws or practices which they object such as the USA Patriot Act (think privacy “sanctuary” cities). Third, using case studies of Seattle, Oakland, and NYC, that cities are actively engaged in all three areas. And, finally, that cities can and should do more.
March 1: Luise Papcke — Project on (Collaborative) Filtering and Social Sorting
ABSTRACT: I am working on a larger project about (collaborative) filtering and social sorting and how it challenges, or supports, various tenets of liberal theory. There is of course copious theoretical work about the nature of surveillance, interrogating for instance how we have moved from the Benthamian/Foucauldian panopticon style surveillance to the ban-opticon(Bigo) of keeping people out on the basis of information about them and/or synopticon(Matthiesen) describing how we are implicated in the surveillance of ourselves and of each other. All models describe how the new wealth of information is used to reinforce old or establish new discriminatory patterns in the market place, in social contexts and in governmental practices. Due to recent electoral results, the debate about how filtering of news information may have contributed to 'bubbles' and a further erosion of the already polarized public discourse has if anything intensified. In this part of my project, I survey the practices of social sorting. I take a closer look at how the categorization of citizens that is at the basis of surveillance practices actually works and what effects that has on the equal standing of citizens in the public and civic spheres. What data collected by which (bureaucratic/ governmental) institutions come to play a role for how citizens can pursue their interests in the political and civic spheres? Which classifications affect citizenship standing the most, and are they simply a reinforcement of ‘old’ discriminatory patterns, or contain significant new elements? Finally, is it reasonable to distinguish between public institutions and private third parties making discriminations on the basis of such categorization, given that discriminatory treatment in the market may have very strong effects on citizenship standing? This part of the project being still rather early-stage, my presentation will map out the different categorization models to discuss what categorization practices seem especially detrimental to equal citizenship standing.
February 22: Yafit Lev-Aretz and Grace Ha (in collaboration with Katherine Strandburg) — Privacy and Innovation
ABSTRACT: Calls to limit or refrain from privacy regulation rest on a variety of conflicting grounds, such as freedom of speech, safety, security, efficiency, and innovation. One of the most widely cited, but least clearly specified, such grounds is the stifling effect that privacy regulation is said to have on innovation. Regulatory intervention for the sake of privacy, goes the claim, is suspect because it will hinder the development of a variety of socially valuable and innovative products, technologies or business models.[i] The threat of stifled innovation is often invoked in essentially talismanic fashion by those opposed to privacy regulation, without evidence and with little detail as to precisely what kind of innovation is at risk, the nature and severity of the looming risk, or by what mechanism any particular regulatory proposal would make the risk materialize.[ii] Privacy scholarship also has devoted surprisingly little attention to these questions.[iii] In this project, we interrogate and analyze the interplay between privacy regulation and innovation, drawing upon insights from the privacy, innovation and regulatory literatures. In particular, we set the debate about privacy regulation and innovation into the context of studies of the effects of regulation on innovation in other arenas, such as health care, environmental policy and consumer safety.[iv] We show that the bare argument that privacy regulation will “stifle” innovation is overly simplistic. Innovation is not a commodity of which society simply has “more” or “less.” Like many other aspects of the legal and economic background within which innovation occurs, regulation shapes innovation and affects its direction and character as much as it affects the amount of innovation that occurs. Moreover, the implications of regulation for innovation will depend, in the privacy arena as elsewhere, on the design of the regulation.[v] While we do not deny that there may be normative tradeoffs to be made between certain types of innovation and certain instantiations of privacy values, we argue that privacy regulation cannot be pigeonholed exclusively as an enemy of technological development. Indeed, privacy may be an essential catalyst for innovation. Thus, viewing the relationship between privacy and innovation simplistically, as a zero-sum trade-off, does a disservice to the social importance of both. We set off by mapping and categorizing the contentions that have been made about the effect of privacy regulation on innovation during previous debates about privacy regulation. While some of the possible arguments are unique to privacy regulation, others are classic counter-regulation arguments that are generally unpersuasive without a concrete cost-benefit analysis tailored to a particular situation.[vi] We then disentangle and characterize the various ways in which regulation can interact with innovation. The relationship between privacy regulation and innovation may involve a variety of regulatory means and innovation systems. We home in on issues such as the direction of the putatively stifled innovation, the particular types of innovation that may be stifled, the possibility that regulation can re-direct innovation in socially desirable directions, the possibility of innovation in means for regulatory compliance, mechanisms connecting specific regulatory avenues with particular effects on innovation and the nature of the social costs and benefits that might emerge from these interactions. Beginning with existing literature in privacy and other fields, we also explore the various available regulatory design levers that affect how regulation and innovation interact. While the relationship between privacy regulation and innovation has much in common with the relationship between regulation and innovation more broadly, we also consider how a more careful analysis of the relationship between privacy and innovation might play out in particular regulatory debates in the privacy arena. For example, privacy regulation’s long-standing reliance on a notice and consent regime has been the subject of almost universal critique based on its effectiveness in protecting privacy.[vii] Here we consider the implications of the significant gap between compliance with notice and consent based regulation and the effective promotion of privacy values for innovation. Notice and consent regulation may be both ineffective and wasteful, misleading individual consumers about their privacy and prompting expenditure of resources on compliance measures that do not promote privacy goals.[viii] Other examples include the possibility that regulation promoting “privacy by design,” in which privacy protection measures are integrated into the software, might be a spur for privacy-enhancing innovation and the opposite possibility that certain types of privacy regulation might divert resources away from innovation in privacy-preserving technologies and toward regulatory compliance initiatives.
[i] See, e.g., Richard Waters, Google Says Tighter EU Search Regulations Would ‘Hurt’ Innovation, The Financial Times, June 24, 2013; Colleen Taylor, Google Co-Founders Talk Regulation, Innovation, and More in Fireside Chat with Vinod Khosla, TechCrunch, Jul. 6, 2014, https://techcrunch.com/2014/07/06/google-co-founders-talk-long-term-innovation-making-big-bets-and-more-in-fireside-chat-with-vinod-khosla/; Adam Thierer & Ryan Hagemann, Removing Roadblocks to Intelligent Vehicles and Driverless Cars, 5 Wake Forest J.L. & Pol’y 339, 349 (2015).
[ii] See Julie E. Cohen, The Surveillance-Innovation Complex: The Irony of the Participatory Turn, in The Participatory Condition 10 (Darin Barney et. al. eds., 2015).
[iii] But see, e.g., Avi Goldfarb & Catherine Tucker, Privacy and Innovation, 12 Innovation Pol’y & the Economy 65, 77 (2012) (noting that privacy regulations will likely restrict innovation in the domain of the advertising-supported Internet) [‘Goldfarb and Tucker’]; Tal Z. Zarsky, The Privacy-Innovation Conundrum, 19 Lewis & Clark L. Rev. 115, 140-41 (2015) (stating that stronger privacy protections will reduce innovation).
[iv] See, e.g., Matthew Grennan & Robert Town, The FDA and the Regulation of Medical Device Innovation: A Problem of Information, Risk, and Access, 4 Penn Wharton Public Policy Initiative 1 (2016) (discussing the relationship between FDA regulations on coronary stents and consumer safety); Rebecca S. Eisenberg, Reexamining Drug Regulation from the Perspective of Innovation Policy, 160 J. Institutional & Theoretical Economics (JITE) 126 (2004) (discussing the impact of FDA regulations on new drug development); David Popp, Innovation and Climate Policy, 2 Annual Review of Resource Economics 283 (2010) (describing the impact of environmental regulations on the development of clean technologies).
[v] Dennis D. Hirsch & Ira S. Rubinstein, Better Safe than Sorry: Designing Effective Safe Harbor Programs for Consumer Privacy Legislation, 10 BNA Privacy & Security Law Report 1639, 1643-46 (2011).
[vi] See, e.g., Goldfarb & Tucker, at 77; Rahul Telang, A Privacy and Security Policy Infrastructure for Big Data, 10 I/S: J. L. & Pol’y for Info Soc’y 783 (2015).
[vii] See, e.g., Daniel J. Solove, Privacy Self-Management and the Consent Dilemma, 126
Harv. L. Rev. 1880 (2013); James P. Nehf, Open Book: The Failed Promise
of Information Privacy in America 191 (2012); Richard Warner, Undermined Norms: The Corrosive Effect of Information Processing Technology on Informational Privacy, 55 St. Louis L.J. 1047, 1084–86 (2011).
[viii] See Protecting Consumer Privacy in an Era of Rapid Change (2010 FTC Report), available at http://www.ftc.gov/os/2010/12/101201privacyreport.pdf.
February 15: Argyri Panezi — Academic Institutions as Innovators but also Data Collectors - Ethical and Other Normative Considerations
ABSTRACT: In my presentation I wish to discuss the role of academic institutions as innovators particularly when they are involved in data-driven research projects immediately related to members of their community (students, researchers, administration and faculty) but also to their local communities. With research projects on the Internet of Things and on Smart Cities taking off, there is arguably a need to discuss ethics, codes of conduct and perhaps responsibilities when institutions collect and manage different types of data needed for these projects. I am generally interested in the management of digital resources within academia. I define digital resources broadly to include data in digitized form and other digitized material that are machine-readable -thus material in any form that when digitized can ultimately be processed as raw data. Academic institutions have long been familiar with circumstances when their collection of data, incidental (for example for practical, administrative purposes) or purposeful (for research or for archival purposes), is subject to legal and ethical rules. One can look at several examples to draw analogies from, in longstanding practices within academic environments: recruitment and admissions departments storing all kinds of sensitive data collected by candidates, academic libraries having access to data of their readers (which books are checked out), science labs conducting experiments in which members of the student body participate etc. Is the involvement of academia in big-data research projects any different? During the presentation I will try to map the relevant legal issues and also suggest what types of academic research I focus on. A central question is which responsibilities arise when academic institutions partner with industry. There are a number of complex legal issues that arise in this context: an interesting mix of access issues (IP considerations), data protection, and security issues. To exemplify the complexity I will also be presenting an example coming from my current research in digitization.
February 8: Katherine Strandburg — Decisionmaking, Machine Learning and the Value of Explanation
ABSTRACT: Much of the policy and legal debate about algorithmic decision-making has focused on issues of accuracy and bias. Equally important, however, is the question of whether algorithmic decisions are understandable by human observers: whether the relationship between algorithmic inputs and outputs can be explained. Explanation has long been deemed a crucial aspect of accountability, particularly in legal contexts. By requiring that powerful actors explain the bases of their decisions — the logic goes — we reduce the risks of error, abuse, and arbitrariness, thus producing more socially desirable decisions. Decision-making processes employing machine learning algorithms complicate this equation. Such approaches promise to refine and improve the accuracy and efficiency of decision-making processes, but the logic and rationale behind each decision often remains opaque to human understanding. Indeed, at a technical level, it is not clear that all algorithms can be made explainable and, at a normative level, it is an open question when and if the costs of making algorithms explainable outweigh the benefits. This presentation will begin to map out some of the issues that must be addressed in determining in what contexts, and under what constraints, machine learning approaches to governmental decision-making are appropriate
February 1: Argyro Karanasiou — A Study into the Layers of Automated Decision Making: Emergent Normative and Legal Aspects of Deep Learning
ABSTRACT: The paper dissects the intricacies of Automated Decision Making (ADM) and urges for refining the current legal definition of AI when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. ADM relies upon a plethora of algorithmic approaches and has already found a wide range of applications in marketing automation, social networks, computational neuroscience, robotics, and other fields. Whilst coming up with a toolkit to measure algorithmic determination in automated/semi-automated tasks might be proven to be a tedious task for the legislator, our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm; this can take various shapes and thus yield different answers to key issues regarding agency. The paper offers a fresh look at the concept of “Machine Intelligence”, which exposes certain vulnerabilities in its current legal interpretation. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of Human – Machine interaction and can thus serve as a point of reference for outlining distinct rights and obligations of the programmer and the consumer: driverless cars are used as a case study to explore the several layers of human and machine interaction. These different degrees of automation reflect various levels of complexities in the underlying algorithms, and pose very interesting questions in terms of regulating the algorithms that undertake dynamic driving tasks. Part 2 further discusses the intricate nature of the underlying algorithms and artificial neural networks (ANN) that implement them and considers how one can interpret and utilize observed patterns in acquired data. Finally, the paper explores the scope for user empowerment and data transparency and discusses attendant legal challenges posed by these recent technological developments.
January 25: Scott Skinner-Thompson — Equal Protection Privacy
ABSTRACT: To the extent the right to privacy exists, it is often understood as universal. If not universal, then of particular importance to marginalized individuals. But in practice, people of privilege tend to fare far better when they bring privacy tort claims than do non-privileged individuals. This, despite doctrine suggesting that those who occupy prominent and public social positions are entitled to diminished privacy tort protections. This Article unearths disparate outcomes in public disclosure tort case outcomes, and uses the unequal results as a lens to expand our understanding of how constitutional equality principles might be used to rejuvenate beleaguered privacy tort law. Scholars and the Supreme Court have long recognized that state action applies to the common law, both because judges make the substantive rule of decision and enforce the law. Under this theory of state action, the First Amendment has been used as a means of limiting the extent of privacy and defamation torts. But if state action applies to tort law, should other constitutional provisions bear on the substance of common law torts? This Article argues that the answer is yes, and uses the unequal implications of prevailing public disclosure tort doctrine to explore whether constitutional equality principles can be used to reform the currently weak protections provided by black letter privacy tort law. By so doing, the Article also opens a doctrinally-sound basis for a broader discussion of how constitutional liberty, due process, and equality norms might influence tort law across a variety of substantive contexts.
December 7: Tobias Matzner — The Subject of Privacy
ABSTRACT: The paper engages with theories which establish the value of privacy. It compares two accounts of privacy: the first as protecting a particular, private space like the home or the “private sphere”, and the second as the relative separation of social contexts. Most theories of the value of privacy pertain to the first category, where privacy is seen as necessary space for an autonomous subject. Using various examples from current privacy research as well as normative positions, the paper shows that this focus on autonomy is problematic. Thus, it is shown that the second account of privacy is much better suited to grasp the problems brought about by digital media. The paper continues to show that the second account of privacy is often linked to the idea of “identity management”; i.e. privacy is not only meant to separate social contexts, but also to clear a space where free decisions about the personalities one assumes in this contexts can be taken. Such a view implies the first account of privacy within the second. Based on theories of Hannah Arendt and Judith Butler, the paper develops an alternative account of privacy and personality that better fits the problems of digital communication. Examples from empirical studies of teenagers’ behavior online illustrate how the implicit individualism in “identity management” can lead to victim blaming. The paper concludes by showing how the value of privacy can be conceived from this perspective. Rather than providing freedom in the sense of autonomy privacy protects the freedom to be someone else in the future or at other places – which however need not necessarily be an autonomous person. Thus, privacy eventually protects the fundamental value of plurality.
November 30: Yafit Lev-Aretz — Data Philanthropy
ABSTRACT: Everybody is busy collecting. The business of collecting data and extracting insights in pursuit of specified goals has never been more thriving. The privacy and security implications are terrifying: unlimited information about virtually anyone and anything is being recorded and archived in data banks that are subject to a variety of cyber threats. But alongside the risks lies an enormous opportunity: troves of data represent a boundless wealth of potential insights for the progress of knowledge and society. When the right information is matched with the right questions, numbers could be translated into real life value by answering pressing questions, mitigating common challenges, and guiding policy decisions. Because data is non-rivalry, the same information could be analyzed for different purposes, and data that has been deemed useless for one could unlock a world of possibilities for another. Advocates of data sharing have been calling on private sector actors to voluntarily share their data for social impact. Robert Kirkpatrick, the head of the UN Global Pulse Initiative, an R&D lab that uses big data and real-time analytics to make policymaking more agile and effective, explained that “the public sector cannot fully exploit Big Data without leadership from the private sector.” And stressed: “what we need is action that goes beyond corporate social responsibility.” Similarly, Matt Stempeck, Microsoft’s Director of Civic Technology in New York City, wrote: “Companies shaping this data-driven world can contribute to the public good by working directly with public institutions and social organizations to bring their expertise and information assets to bear on shared challenges.” In many instances, this kind of giving has been termed “data philanthropy.” Following a comprehensive introduction to the data philanthropy discourse, this project aims at providing a better understanding of data collaborations, sharing incentives, and practical concerns. Subsequently, using the Fair Information Practices Principles framework, the project will submit a set of policy recommendations to capitalize on the potential of data givings while minimizing risks that could result in from such collaborations.
November 16: Helen Nissenbaum — Must Privacy Give Way to Use Regulation?
ABSTRACT: In a departure from traditional modes of privacy regulation, there is growing support for regulating only certain uses of personal information while entirely deregulating its collection. Proponents argue that the safeguards usually associated with privacy protection can be achieved through judicious constraints on use, so that ex ante constraints on collection will not stifle the enormous potential of AI and big data. My paper questions this increasingly popular logic not only because it is ambiguous to the point of incoherence or plays suspiciously well with the dominant business model of information industry incumbents. Although there is no denying the genuine and unprecedented challenges to privacy posed by data science, the paper argues that fully substituting restrictions on collection with use restrictions will weaken one of the cornerstones of a free society with little assurance of public welfare gains.
November 9: Bilyana Petkova — Domesticating the "Foreign" in Making Transatlantic Data Privacy Law
ABSTRACT: Research shows that in the data privacy domain, the regulation promoted by frontrunner states in federated systems such as the United States or the European Union generates races to the top, not to the bottom. Institutional dynamics or the willingness of major interstate companies to work with a single standard generally create opportunities for the federal lawmaker to level up privacy protection. This article uses federalism to explore whether a similar pattern of convergence (toward the higher regulatory standard) emerges when it comes to the international arena, or whether we witness a more nuanced picture. I focus on the interaction of the European Union with the United States, looking at the migration of legal ideas across the (member) state jurisdictions with a focus on breach notification statutes and privacy officers. The article further analyses recent developments such as the invalidation of the Safe Harbor Agreement and the adoption of a Privacy Shield. I argue that instead of a one-way street, usually conceptualized as the EU ratcheting up standards in the US, the influences between the two blocs are mutual. Such influences are conditioned by the receptivity and ability of domestic actors in both the US and the EU to translate, and often, adapt the “foreign” to their respective contexts. Instead of converging toward a uniform standard, the different points of entry in the two federated systems contribute to the continuous development of two models of regulating commercial privacy that, thus far, remain distinct.
November 2: Scott Skinner-Thompson — Recording as Heckling
ABSTRACT: There are increasing calls for a right to public privacy, and often such calls are justified with reliance on the First Amendment. Similarly, there is a growing body of authority recognizing that recording of public space is also protected by the First Amendment. Both purported rights serve important First Amendment values—recording information can be critical to future speech and, as a form of confrontation to authority, is also a direct form of expression. Likewise, functional efforts to maintain privacy while navigating public space may help create an incubator for thought and future speech, and can also serve as a form of direct expressive resistance to surveillance regimes. But while recordings may be critical to government accountability and have important First Amendment benefits, they also have obvious privacy implications. How do we balance the right to record with the right to maintain privacy? When can the government regulate recording that attempts to breach the privacy shields erected by other citizens? I suggest that the concept of the heckler’s veto provides a promising rubric for analyzing attempts to regulate these sometimes competing forms of “speech.” This piece argues that just as a heckler’s suppression of another’s free speech justifies government regulation of the heckler’s speech, so too when recording (a form of speech) infringes on and pierces reasonable efforts to maintain privacy (also a form of speech), then the government may—through direct regulation or even tort law—limit the ability to record.
October 26: Yan Shvartzhnaider — Learning Privacy Expectations by Crowdsourcing Contextual Informational Norms
ABSTRACT: Designing programmable privacy logic frameworks that correspond to social, ethical, and legal norms has been a fundamentally hard problem. The theory of Contextual integrity (CI) (Nissenbaum 2010) offers a model for conceptualizing privacy that is able to bridge technical design with ethical, legal, and policy approaches. While CI is capable of capturing the various components of contextual privacy in theory, it is challenging to discover and formally express these norms in operational terms. In this talk I will discuss our work in designing a framework for crowdsourcing privacy norms based on the theory of contextual integrity.
October 19: Madelyn Sanfilippo — Privacy and Institutionalization in Data Science Scholarship
ABSTRACT: Meta-analysis of methodological institutionalization across three scholarly disciplines provides evidence that not only are traditional statistical quantitative methods more institutionalized and consistent, but also are drawn on to structure data scientific approaches when institutionalization is sought for new and large n quantitative methods. Among the strategies, norms, and rules within this body of literature are various institutionalisms surrounding issues of privacy, with stark contrasts in level of detail and attitudes–such as compliance versus privacy as a social value—based on discipline and methodological approaches. This talk will focus on key insights from recently completed work on institutionalization in data science scholarship and outline preliminary findings from work-in-progress pursuing insight into attitudinal and institutional differences reflected in this literature toward privacy.
October 12: Paula Kift — The Incredible Bulk: Metadata, Foreign Intelligence Collection, and the Limits of Domestic Surveillance Reform
ABSTRACT: On June 2, 2015 Congress passed the USA FREEDOM Act, which, among other things, was intended to end the bulk collection of domestic telephony metadata that the National Security Agency (NSA) had been conducting under the authority of Section 215 of the USA PATRIOT Act. The metadata program sparked outrage among privacy and civil liberties advocates across the United States since it implied that, in the course of foreign intelligence investigations, the U.S. government was collecting the communication records of millions of Americans in bulk, in the absence of any particularized suspicion. The reliance on Section 215 of the PATRIOT Act as the legal basis for the program also raised significant statutory and constitutional concerns. This paper analyzes whether the passage of the USA FREEDOM Act was able to alleviate some of these these concerns. It argues that, even though the FREEDOM Act made some headway towards limiting the scope, and improving the accountability, of domestic government surveillance programs, a significant risk remains that the U.S. government can continue collecting large amounts of communications metadata of Americans that are not strictly relevant to any authorized investigations. Most worryingly, the U.S. government may have simply shifted the bulk collection of domestic metadata to a different authority, sweeping up the telecommunication records of millions of Americans at home under the guise of foreign intelligence collection abroad.
October 5: Craig Konnoth — Health Information Equity
ABSTRACT: As of the last few years, the health information of numerous Americans is being collected and used for follow-on, secondary research to study correlations between medical conditions, genetic or behavioral profiles, and treatments. Recent federal legislation and regulations make it easier to use the data of the low income, unwell, and elderly, than that of others, for this research. This imposes disproportionate security and autonomy burdens on these individuals. Those who are well off and pay out of pocket can effectively exempt their data from the publicly available information pot. This presents a problem which modern research ethics is not well equipped to address. Where it considers equity at all, it emphasizes underinclusion and the disproportionate distribution of research benefits, rather than overinclusion and disproportionate distribution of burdens. I rely on basic intuitions of reciprocity and fair play, as well as broader accounts of social and political equity to show that equity in burden distribution is a key aspect of the ethics of secondary research. To satisfy its demands we can use three sets of regulatory and policy levers. First, information collection for public research should expand beyond groups having the lowest welfare. Next, data analyses and queries should more equitably draw on data pools. Finally, we must create an entity to coordinate these solutions using existing statutory authority if possible. Considering health information collection at a systematic level rather than that of individual clinical encounters gives us insight into the broader role health information plays as a site of personhood, citizenship, and community.
September 28: Jessica Feldman — the Amidst Project
ABSTRACT: In this talk I will discuss the amidst project -- an ad-hoc, peer-to-peer, encrypted network for mobile phones -- and the fieldwork that led me to work on it. Drawing on 50+ interviews and surveys with activists, human rights workers, journalists, and engineers in Cairo, Istanbul, Madrid, and New York City, my doctoral dissertation considers surveillance, blocking, and alternate communications methods in the "movements of the squares" and their aftermath. As a response to this fieldwork, I am working with a team of engineers on the amidst network. As a mobile "mesh" network, amidst comes into being when a large group of people are assembled together, and uses each phone as a node to build the network, attempting to provide a solution to the problems of just-in-time blocking and infrastructural surveillance allowed for by centralized telecom. The project also experiments with decentralized, non-hierarchical, localized communication and security practices, which bring about some interesting problems, both philosophically and technically, regarding the fraught relationships among privacy, trust, accountability, and democratic publics.
September 21: Nathan Newman — UnMarginalizing Workers: How Big Data Drives Lower Wages and How Reframing Labor Law Can Restore Information Equality in the Workplace
ABSTRACT: While there has been a flurry of new scholarship on how employer use of data analysis may lead to subtle but potentially devastating individual discrimination in employment systems, there has been far less attention to the ways the deployment of big data may be driving down wages for most workers, including those who manage to be hired. This article details the ways big data can and in many cases is actively being deployed to lower wages through hiring practices, in the ways raises are now being offered, and in the ways workplaces are organized (and disorganized) to lower employee bargaining power—and how new interpretations of labor law are beginning to and can in the future reshape the workplace to address these economic harms. Data analysis is increasingly helping to lower wages in companies beginning in the hiring process where pre-hire personality testing helps employers screen out employees who will agitate for higher wages and organize or support unionization drives in their companies. For employees who are hired, companies have massively expanded data-driven workplace surveillance that allows employers to assess which employees are most likely to leave and thereby limit pay increases largely to them, lowering wages over time for workers either less able to find new employment because of their age or less inclined in general to risk doing so. Data analysis and so-called “algorithmic management” has also allowed the centralized monitoring of far flung workers organized nominally in subcontractors or as individual contractors, while traditional firms such as in retail implement data-driven scheduling that resembles the “on-demand” employment of independent contractors. All of this shifts risk and “downtime” costs to employees and lowers their take-home pay, even as the fragmenting of the workplace makes it harder for workers to collectively organize for higher wages. The article addresses how we should rethink and interpret existing labor law in each of these aspects of the employment process. The NLRB can reasonably construe many pre-hire employment tests as violating federal labor law’s prohibition of screening out union sympathizers, much as the EEOC has found many personality tests violate the Americans with Disabilities Act by allowing indirect identification of people with mental illness. Similarly, since big data analysis can reveal pro-union sympathies of current employees, under existing prohibitions of “polling” employees for their views, a reasonable extension of the law would be to prohibit sharing any personal data collected by management that might reveal protected conduct or union sympathies with line managers or outside management consultants involved in advising in labor campaigns. The Board can also level the informational playing field by making both hiring algorithms and those determining pay increases more available during collective bargaining. The Board is already moving to expand its “joint employer” doctrine to allow workers to challenge the fragmented workplace increasingly driven by algorithmic management and a clear recognition that algorithms establish exactly the control of nominally independent contractors or subcontractor’s workers that entitle them to collective bargaining rights with a central employer, strengthening worker bargaining power. Such a “collective action” approach to the problem is far more likely to succeed than other proposals focused on strengthening individual worker privacy or anti-discrimination rights in the workplace in regards to data-driven decision-making. As scholars have noted, disadvantaged groups under the civil rights laws may have sharply different preferences in wage versus benefit packages, so a process that increases informational resources for all workers and allows them to negotiate together for the mix of wages, benefits, work conditions and other “public goods” in the workplace, including privacy protections, will better reflect the overall interests of employees than in either a classic economic model based on a marginal worker’s “exit” or a “rights consciousness” litigation approach to rein in individual employment harms. In making this overall argument, the article partially addresses the debate on why wages have stagnated and even fallen below productivity gains over the last four decades as the deployment of data technology has played a significant and growing role in helping employers extract a disproportionate share of employee productivity gains to the benefit of management and shareholders.
September 14: Kiel Brennan-Marquez — Plausible Cause
ABSTRACT: “Probable cause” is not about probability. It is about plausibility. To determine if an officer has the requisite suspicion to perform a search or seizure, what matters is not the statistical likelihood that a “person, house, paper or effect” is linked to criminal activity. What matters is whether criminal activity provides a convincing explanation of observed facts. For an inference to qualify as plausible, an observer must understand why the inference follows; she must be able to explain its relationship to the facts. Probable inferences, by contrast, do not require explanations. An inference can be probable—in a predictive sense, based on past trends—without a human observer understanding what makes it so. In many cases, plausibility and probability overlap. An inference that accounts for observed facts is often likely to be true, and vice versa. But there is an important sub-set of cases in which the two properties pull apart, raising deep questions about the underpinnings of Fourth Amendment suspicion: inferences generated by predictive algorithms. In this Article, I argue that casting suspicion in terms of plausibility, rather than probability, is both more consistent with established law and crucial to the Fourth Amendment’s normative integrity. Before law enforcement officials may intrude on private life, they must explain why they believe wrongdoing has occurred. This “explanation-giving” requirement has two key virtues. First, it facilitates governance; we cannot effectively regulate what we do not understand. Second, it allows judges to consider the “other side of the story”—the innocent version of events a suspect might offer on her own behalf—before warranting searches and seizures. In closing, I connect these virtues to broader themes of democratic theory. In a free society, legitimacy is not measured solely by outcomes. The exercise of state power must be explained—and the explanations must be responsive both to the democratic community writ large and to the specific individuals whose interests are infringed.
April 27: Yan Schvartzschnaider — Privacy and loT AND Rebecca Weinstein - Net Neutrality's Impact on FCC Regulation of Privacy Practices
April 20: Joris van Hoboken — Privacy in Service-Oriented Architectures: A New Paradigm? [with Seda Gurses]
April 13: Florencia Marotta-Wurgler — Who's Afraid of the FTC? Enforcement Actions and the Content of Privacy Policies (with Daniel Svirsky)
April 6: Ira Rubinstein — Big Data and Privacy: The State of Play
March 30: Clay Venetis — Where is the Cost-Benefit Analysis in Federal Privacy Regulation?
March 23: Diasuke Igeta — An Outline of Japanese Privacy Protection and its Problems
Johannes Eichenhofer — Internet Privacy as Trust Protection
March 9: Alex Lipton — Standing for Consumer Privacy Harms
March 2: Scott Skinner-Thompson — Pop Culture Wars: Marriage, Abortion, and the Screen to Creed Pipeline [with Professor Sylvia Law]
February 24: Daniel Susser — Against the Collection/Use Distinction
February 17: Eliana Pfeffer — Data Chill: A First Amendment Hangover
February 10: Yafit Lev-Aretz — Data Philanthropy
February 3: Kiel Brennan-Marquez — Feedback Loops: A Theory of Big Data Culture
January 27: Leonid Grinberg — But Who BLocks the Blockers? The Technical Side of the Ad-Blocking Arms Race
November 18: Angèle Christin - Algorithms, Expertise, and Discretion: Comparing Journalism and Criminal Justice
November 4: Solon Barocas and Karen Levy — Understanding Privacy as a Means of Economic Redistribution
October 28: Finn Brunton — Of Fembots and Men: Privacy Insights from the Ashley Madison Hack
October 21: Paula Kift — Human Dignity and Bare Life - Privacy and Surveillance of Refugees at the Borders of Europe
October 14: Yafit Lev-Aretz and co-author, Nizan Geslevich Packin — Between Loans and Friends: On Soical Credit and the Right to be Unpopular
October 7: Daniel Susser — What's the Point of Notice?
September 30: Helen Nissenbaum and Kirsten Martin — Confounding Variables Confounding Measures of Privacy
September 23: Jos Berens and Emmanuel Letouzé — Group Privacy in a Digital Era
September 16: Scott Skinner-Thompson — Performative Privacy
September 9: Kiel Brennan-Marquez — Vigilantes and Good Samaritan
David Krone — Compliance, Privacy and Cyber Security Information Sharing
Edwin Mok — Trial and Error: The Privacy Dimensions of Clinical Trial Data Sharing
Dan Rudofsky — Modern State Action Doctrine in the Age of Big Data
April 22: Helen Nissenbaum — Respect for Context' as a Benchmark for Privacy: What it is and Isn't
April 15: Joris van Hoboken — From Collection to Use Regulation? A Comparative Perspective
March 11: Rebecca Weinstein (Cancelled)
Kristen Martin — Transaction costs, privacy, and trust: The laudable goals and ultimate failure of notice and choice to respect privacy online
Ryan Calo — Against Notice Skepticism in Privacy (and Elsewhere)
Lorrie Faith Cranor — Necessary but Not Sufficient: Standardized Mechanisms for Privacy Notice and Choice
October 22: Matthew Callahan — Warrant Canaries and Law Enforcement Responses
October 15: Karen Levy — Networked Resistance to Electronic Surveillance
October 8: Joris van Hoboken — The Right to be Forgotten Judgement in Europe: Taking Stock and Looking Ahead
October 1: Giancarlo Lee — Automatic Anonymization of Medical Documents
September 24: Christopher Sprigman — MSFT "Extraterritorial Warrants" Issue
September 17: Sebastian Zimmeck — Privee: An Architecture for Automatically Analyzing Web Privacy Policies [with Steven M. Bellovin]
September 10: Organizational meeting
January 29: Organizational meeting
November 20: Nathan Newman — Can Government Mandate Union Access to Employer Property? On Corporate Control of Information Flows in the Workplace
September 25: Luke Stark — The Emotional Context of Information Privacy
September 18: Discussion — NSA/Pew Survey
September 11: Organizational Meeting
April 10: Katherine Strandburg — ECPA Reform; Catherine Crump: Cotterman Case; Paula Helm: Anonymity in AA
March 27: Privacy News Hot Topics — US v. Cotterman, Drones' Hearings, Google Settlement, Employee Health Information Vulnerabilities, and a Report from Differential Privacy Day
March 6: Mariana Thibes — Privacy at Stake, Challenging Issues in the Brazillian Context
March 13: Nathan Newman — The Economics of Information in Behavioral Advertising Markets
February 27: Katherine Strandburg — Free Fall: The Online Market's Consumer Preference Disconnect
February 20: Brad Smith — Privacy at Microsoft
February 13: Joe Bonneau — What will it mean for privacy as user authentication moves beyond passwo
February 6: Helen Nissenbaum — The (Privacy) Trouble with MOOCs
January 30: Welcome meeting and discussion on current privacy news
November 14: Travis Hall — Cracks in the Foundation: India's Biometrics Programs and the Power of the Exception
September 19: Nathan Newman — Cost of Lost Privacy: Google, Antitrust and Control of User Data