Privacy engineering seeks to provide tools and methods to design privacy-preserving systems or patch privacy invasive ones. Obfuscation is one of the essential tools in the privacy engineering toolkit. But what can we learn from the plethora of methods and techniques that one may categorize as obfuscation? What can we learn from the role obfuscation plays in privacy engineering? In this talk, Ero Balsa will provide an overview of the two main reasons why privacy engineers resort to obfuscation: to enable people to protect themselves against unnecessarily privacy-invasive systems, and to modulate the level of exposure that providing utility to untrusted parties requires.
From July to January of 2020, Stanford Internet Observatory researchers worked with a coalition of researchers, government entities, tech companies, and civil society organizations in a multi-stakeholder partnership called the Election Integrity Partnership (EIP). Its mission was to rapidly detect high-velocity and potentially impactful false and misleading narratives related to voting. This talk will discuss findings from the partnership: how incidents became narratives, the rise of bottom-up misinformation, the dynamics of repeat spreaders, and the way in which platform policies shape message propagation.
Cryptographic proof of liabilities (PoL) is a cryptographic primitive to prove the size of funds a bank owes to its customers in a decentralized manner and can be used for solvency audits with better privacy guarantees. Most PoL schemes follow the same principle, i.e., a prover aggregates all of the user balances and enables users to verify balance inclusion in the reported total amount. This process is probabilistic and the more the users who verify inclusion the better the guarantee of a non-cheating prover. In this presentation, Yan Ji introduces generalized PoL, which was originally proposed for proving financial solvency, by extending the state-of-the-art PoL scheme with extra privacy features, and making it applicable to domains outside finance, including transparent and private donations, new algorithms for disapproval voting and negative reviews, and publicly verifiable COVID-19 cases.
This is an early-stage project about misinformation. While legal scholars have been active over the last 4 years identifying legal definitions of and developing legal responses to the problem of misinformation, including assessing the constitutionality of those responses under the current Supreme Court's First Amendment jurisprudence, less attention has been paid to how the law is already changing as a result of misinformation and how current legal doctrines and institutions are vulnerable to erosion because of misinformation already in the mix. This project brings together literature in sociology and social network theory about how information spreads and doctrinal standards used in judicial review of government action.
Thinking Backwards from Improvement in Information Technology Action Research
In information technology for development and related fields, action-oriented researchers aim to design and evaluate how technology can be used to improve the lives of underserved populations around the globe. However, improvement is a value-laden concept with normative, causal, and methodological assumptions. There are many alternative definitions that can be difficult to engage with and tempting for an action researcher to ignore. However, these definitions can heavily influence the direction, design, and evaluation of such work. In this presentation, Anthony Poon discusses some potential perspectives on improvement, including human development, empowerment, and post-development, and how they have influenced some of his past and current work.
Debate: "Does AI Pose an Existential Threat to Humanity?"
DLI's inaugural debate was inspired by thinking through the provocations posed by the impact of ‘intelligent’ technologies on the future of human life. Will robots take over the planet? Will they undermine or erode what it means to be human in other more subtle or unanticipated ways? Is the preoccupation with intelligent machines a red herring? Or is the biggest threat posed by intelligent machines the affordances they provide to the humans who wield them?
The Unbearable Lightness of Teaching Responsible Data Science
Although an increasing number of ethical data science and AI courses is available, pedagogical approaches used in these courses rely exclusively on texts rather than on algorithmic development or data analysis. Technical students often consider these courses unimportant and a distraction from the “real” material. To develop instructional materials and methodologies that are thoughtful and engaging, we must strive for balance: between texts and coding, between critique and solution, and between cutting-edge research and practical applicability. In this talk, Julia Stoyanovich will discuss responsible data science courses that she has been developing and teaching to technical students at New York University since 2019, and will also speak to some ongoing work on teaching responsible data science to members of the public in a peer learning setting.
Can games make the world a better place? Is it possible that we use games to make a difference in global challenges such as climate change or public health? Can we reduce societal biases, or encourage people to intervene in situations of danger, such as sexual assault? And how do we know the games are doing what they set out to do?
AdTech & Our Privacy – Dark present, brighter future?
This joint session is about the digital advertising ecosystem: we highlight some of its disturbing practices against users’ privacy, explain the puzzle of lack of GDPR enforcement over its clear data protection violations, provide a glimpse on how a major publisher with a significant ad operation, The New York Times, has been trying to safeguard the privacy of its readers without forgoing revenue, and conclude by looking ahead at the current conversations in the web standards community on how to build an ad ecosystem without ubiquitous tracking.
Design does more than supply the market with new products and services; it can raise provocations, critique existing socio-technical arrangements, seed conversations around matters of concern, and imagine radical alternatives. However, even when design is used as a critical provocation or political contestation, the focus is often on ‘making’ something new - a product, interface or artifact. That is because ‘unmaking’, a natural aspect of the designerly transformations always underway in the worlds around us, remains invisibilized and rarely theorized as its own explicit and intentional strategy.
Measuring the Unmeasured: New Threats to Machine Learning Systems
Machine learning (ML) is at the core of many Internet services and operates on users’ personal information. The deciding metric for deploying ML models is often test performance, which measures if the models learned the given task well. Test performance, however, does not measure other important properties of ML models such as security vulnerabilities, privacy leakage and compliance with regulations.
The Platform Insurgency: Does Urban Tech Have an Ethics Problem?
Much of urban tech exploits today’s most ethically-charged technologies and business practices—such as indiscriminate location tracking, facial recognition, and gig work to fundamentally reprogram how urban systems function. As these failures become clearer, and broader awareness of systemic injustice in society grows, how can the emerging field of urban tech clarify choices between right and wrong?
The Truth About Fake News: Measuring Vulnerability to Fake News Online
How well can ordinary people do in identifying the veracity of news in real time? Using a unique research design that has involved crowdsourcing popular news articles from both mainstream and suspect news sources that have appeared in the past 24 hours to both ordinary citizens and professional fact checkers, Professor Tucker will report on the individual level characteristics of those likely to incorrectly identify false news stories as true, the results of interventions to attempt to reduce the prevalence of this behavior, and the prospects for crowdsourcing to serve as a viable means for identifying false news stories in real time.
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
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. In this talk, we will discuss the emerging impact urban technologies have on cities. For instance, how would urban technologies impact urban inequality?