Mechanism design is a form of optimization developed in economic theory. It casts economists as institutional engineers, choosing an outcome and then arranging a set of market rules and conditions to achieve it. In this paper, Lee McGuigan, Jake Goldenfein, and Salome Viljoen argue that mechanism design, applied in algorithmic environments, has become a tool for producing information domination, distributing social costs in ways that benefit designers, and controlling and coordinating participants in multi-sided platforms.
International Computer Science Institute | University of California, Berkeley
Taking Responsibility for Someone Else's Code: Studying the Privacy Behaviors of Mobile Apps at Scale
Modern software development has embraced the concept of "code reuse," which is the practice of relying on third-party code to avoid "reinventing the wheel" (and rightly so). While this practice saves developers time and effort, it also creates liabilities: the resulting app may behave in ways that the app developer does not anticipate. This can cause very serious issues for privacy compliance: while an app developer did not write all of the code in their app, they are nonetheless responsible for it. In this talk, I will present research that my group has conducted to automatically examine the privacy behaviors of mobile apps vis-à-vis their compliance with privacy regulations.
Microsoft Research | Jacobs Institute/Cornell Tech (2021)
Modeling COVID with mobility data to understand inequality and guide reopening
In this paper, we develop a model of COVID spread that uses dynamic mobility networks, derived from US cell phone data, to capture the hourly movements of millions of people from local neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants, grocery stores, or religious establishments.
Software has eaten the world and crapped out a dystopia: a place where Abbot Labs uses copyright claims to stop people with diabetes from taking control over their insulin dispensing and where BMW is providing seat-heaters as an-over-the-air upgrade that you have to pay for by the month. Companies have tried this stuff since the year dot, but Thomas Edison couldn't send a patent enforcer to your house to make sure you honored the license agreement on your cylinder by only playing it on an Edison phonograph. Today, digital systems offer perfect enforcement for the pettiest, greediest grifts imaginable.
Annenberg School for Communication | University of Pennsylvania
Seductive Surveillance and Social Change: The Rise of the Voice Intelligence Industry
Drawing from my forthcoming book The Voice Catchers (Yale U Press, early 2021), I pose two key questions about this new development in the United States: How has the voice intelligence industry been able to gain the kind of social traction that has tens of millions of people giving their up voiceprints to so-called “intelligent assistants”? And in the face of this widespread shift to voice bio-profiling, what social policies should concerned citizens advocate to slow the process and implement regulations regarding this new form of surveillance?
Who bears responsibility for the real-world consequences of technology? This question has been unduly complicated for decades by the 1996 legislation that provides immunity from liability to platforms that host third-party content: Section 230 of the Communications Decency Act.
Deepfakes and Adversarial Examples: Fooling Humans and Machines
In this talk, Omid Pouraseed will discuss recent methods for adversarial data manipulation, and mention possible defense strategies against them. Although manipulations of visual and auditory media are as old as media themselves, the recent advent of deepfakes has marked a turning point in the creation of fake content.
Privacy/Disaster: When Information Flows Are Taken Out of Context
Privacy is contextual. Everyday, we manage different contexts and adjust our privacy expectations accordingly. The theory of Contextual Integrity offers a way to capture contextual norms and a heuristic to analyze privacy. This analysis is especially helpful to detect situations in which the system designers take advantage of well-established, contextual privacy expectations, to encourage user disclosures without adhering to governing norms. For example, imagine an app that is marked to you as a patient/doctor communication tool in a medical context, yet it is actually an insurance company trying to get more information on you.
Fairness & Interpretability in Machine Learning and the Dangers of Solutionism
Supervised learning algorithms are increasingly operationalized in real-world decision-making systems. Unfortunately, the nature and desiderata of real-world tasks rarely fit neatly into the supervised learning contract.
Over the last decade, behavioral science made significant progress and impact in academic research as well as impacted policy in commercial organizations and governments. At the same time, the rise of digital technologies and the digital economy provides exciting opportunities and presents challenges for the next decade of behavioral science. In this talk, Sobolev will explore novel avenues for behavioral science research in the digital economy.
Smart Cities/Digital Neighborhoods: Privacy, Equality, and Adoption of Urban Technologies
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Manage Fields SortFilter The idea of the Smart City is becoming central to the adoption of technologies that enhance and regulate urban spaces. At the same time, smart cities bring about new challenges, as diverse populations interact with an array of new technologies, most of them are based on large-scale data collection and with increasing effects on residents lives.