CITP

Co-sponsored by CITP and Princeton Language and Intelligence

In-person attendance is open to Princeton University faculty, staff, students and alumni. Lunch will be available at noon.

This talk is also available via Zoom.

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Social scientists frequently perform statistical decompositions of wage gaps, attributing group differences in wages to group differences in worker characteristics. Since the survey datasets used to estimate these decompositions are small, the included characteristics are typically low-dimensional, e.g. summary statistics about job history. These low-dimensional summaries risk inducing biased estimates. To mitigate this bias, we adapt machine learning methods to summarize worker histories with rich, low-dimensional representations that are learned from data. We take a “foundation model” approach, first training representations on a dataset of passively-collected resumes before fine-tuning them on the small survey datasets used for wage gap estimation. We discuss an omitted variable bias that can arise in this setting and propose a fine-tuning approach to minimize it. On data from the Panel Study of Income Dynamics, we show that full worker history explains a substantial portion of wage gaps that are unexplained by standard econometric techniques.

With collaborators Susan Athey and David Blei.

Bio: Keyon Vafa is a postdoctoral fellow at Harvard University as part of the Harvard Data Science Initiative. His research focuses on developing machine learning methods to help answer economic questions and also using insights from economics to improve machine learning models. He completed his Ph.D. in computer science at Columbia University in 2023, where he was advised by David Blei. During his Ph.D. he was an NSF GRFP Fellow and Cheung-Kong Innovation Doctoral Fellow. He is a member of the Early Career Board of the Harvard Data Science Review.

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Contributions to and/or sponsorship of any event does not constitute departmental or institutional endorsement of the specific program, speakers or views presented.

If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.

Zoom: https://princeton.zoom.us/j/97144681849

In-person attendance is open to Princeton University faculty, staff, students and alumni. The Zoom will be open to the public.

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Algorithmic predictions are increasingly used to inform the allocations of goods and services in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into the likelihood of future events in order to improve decision making quality, and enhance social welfare. However, if maximizing welfare is the question, to what extent is improving prediction the best answer?

In this talk, we discuss various attempts to contextualize the relative value of algorithmic predictions through both theory and practice. The goal of the first part will be to formally understand how the welfare benefits of improving prediction compare to those of expanding access when distributing social goods. In the latter half, an empirical case study will be presented illustrating how these issues play out in the context of a risk prediction system used throughout Wisconsin public schools.

Bio: Juan Carlos Perdomo is currently a postdoctoral fellow at Harvard University’s Center for Research on Computation and Society. His research centers on the theoretical and empirical foundations of machine learning. Perdomo is particularly interested in studying the downstream consequences, and feedback loops, that arise when predictions are used to make decisions about people.

Perdomo received his Ph.D. from the University of California, Berkeley and his bachelor’s degree in computer science and math from Harvard College.

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This seminar will be recorded and posted to the CITP website, Media Central and YouTube.

If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.

In-person attendance is open to Princeton University faculty, staff, students and alumni. This talk will not be livestreamed or recorded.

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The opacity, unpredictability, and inscrutability of recent computer systems continues to challenge users and regulators. While one approach has been to work toward explainability and transparency, this tactic is expensive and has not yet proven to be broadly successful. This talk proposes that recent developments in artificial intelligence and machine learning might instead be usefully understood as a new “style” of knowledge in the manner used by historian Ian Hacking (1990) to explain the role of technology in the history of science. Rather than containing a nugget of information meant to be revealed from inside a black box, in this way of thinking these systems might be a new form of argumentation and delegation in themselves. This suggests a different emphasis for future policymaking.

Bio: Christian Sandvig is a visiting professor at the School of Social Science of the Institute for Advanced Study (IAS) this academic year. While at IAS, he will work on a book project about the ethics and consequences of algorithmic filtering and machine learning used within online platforms.

Sandvig is also the director of the Center for Ethics, Society, and Computing (ESC) and collegiate professor of Information at the University of Michigan. His research about civil rights and discrimination online has been cited by the US Supreme Court (2021) and the Obama White House (2016). He has worked in the private sector as a software developer in addition to his experience in academia.

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If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.

View the webinar here: https://princeton.zoom.us/j/99981436824

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The learned behaviors of AI and robot agents should align with the intentions of their human designers. Alignment is necessary for AI systems to be used in many sectors of the economy, and so the process of aligning AI systems becomes critical to study for defining effective AI policy. Toward this goal, people must be able to easily specify, inspect, and model agent behaviors. For specifications, we will consider expert-written reward functions for reinforcement learning (RL) and non-expert preferences for reinforcement learning from human feedback (RLHF). This talk will show evidence that experts are bad at writing reward functions: even in a trivial setting, experts write specifications that are overfit to a particular RL algorithm, and they often write erroneous specifications for agents that fail to encode their true intent. It will also show that the common approach to learning a reward function from non-experts in RLHF uses an inductive bias that fails to encode how humans express preferences, and that our proposed bias better encodes human preferences both theoretically and empirically.

Policy implications will be discussed: namely, that engineers’ design processes and embedded assumptions in building AI must be considered. For inspection, humans must be able to assess the behaviors an agent learns from a given specification. A method to find settings that exhibit particular behaviors, like out-of-distribution failures will be discussed. The policy implications for testing AI systems, will be examined, for example through red teaming. Lastly, cognitive science theories attempt to show how people build conceptual models that explain agent behaviors.  Evidence will be shown that some of these theories are used in research to support humans, but that we can still build better curricula for modeling. The policy need for careful onboarding to AI systems will be discussed. The talk will discuss Booth’s current work in the U.S. Senate on responding to the proliferation of AI. Collectively, the research provides evidence that—even with the best of intentions— current human-AI systems often fail to induce alignment, and my research proposes promising directions for how to build better aligned human-AI systems.

Bio: Serena Booth received her Ph.D. at MIT CSAIL in 2023. She studies how people write specifications for AI systems and how people assess whether AI systems are successful in learning from specifications. While at MIT, Booth served as an inaugural Social and Ethical Responsible Computing Scholar, teaching AI Ethics and developing MIT’s AI ethics curriculum that is also released on MIT OpenCourseWare. She is a graduate of Harvard College (2016), after which she worked as an Associate Product Manager at Google to help scale Google’s ARCore augmented reality product to 100 million devices. Booth currently works in the U.S. Senate as a AAAS AI Policy Fellow, where she is working on AI policy questions for the Senate Banking, Housing, and Urban Affairs Committee. Her research has been supported by an MIT Presidential Fellowship and by an NSF GRFP. She is a Rising Star in EECS and an HRI Pioneer.

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This lecture is open to the public.

If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.

Platforms, devices, and algorithms are increasingly weaponized to control and harass the most vulnerable among us. Some of these harms occur at the individual and interpersonal level: for example, abusers in intimate partner violence (IPV) use smartphones and social media to surveil and stalk their victims. Others are more subtle, at the level of social structure: for example, in organizations, workplace technologies can inadvertently scaffold exploitative labor practices.

This talk will discuss research (1) investigating these harms via online measurement studies, (2) building interventions to directly assist survivors with their security and privacy; and (3) instrumenting these interventions as observatories, to enable scientific research into new types of harms as attackers and technologies evolve. The talk will close by sharing a vision for centering inclusion and equity in digital safety, security and privacy, towards brighter technological futures for us all.

Bio: Emily Tseng is a Ph.D. candidate in information science at Cornell University. Her research develops the systems, interventions, and design principles we need to make digital technology safe and affirming for everyone. Tseng’s work has been published at top-tier venues in human-computer interaction (ACM CHI, CSCW) and computer security and privacy (USENIX Security, IEEE Oakland).

For five years, she has worked as a researcher-practitioner with the Clinic to End Tech Abuse, where her work has enabled specialized security services for over 500 survivors of intimate partner violence (IPV). She is the recipient of a Microsoft Research Ph.D. Fellowship, Rising Stars in EECS, Best Paper Awards at CHI, CSCW, and USENIX Security, and third place in the Internet Defense Prize. She previously interned with the Social Media Collective at Microsoft Research, and holds a bachelor’s degree from Princeton University.

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This talk is open to the public.

If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.

Join the webinar here: https://princeton.zoom.us/j/97119221718

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Online platforms like Facebook, Wikipedia, Amazon, and LinkedIn are embedded in the very fabric of our society. They “curate content:” moderate, recommend, and monetize it, and, in doing so, can impact people’s lives positively or negatively. This talk will highlight the need to go beyond how these curation practices are currently designed and tested and argue that academic research can and should guide policy and best practices by discussing two projects.

The first project examines a large natural experiment on Facebook that allowed measuring the causal effect of removing rule-breaking comments on users’ subsequent behavior. The second project investigates the efficacy of “deplatforming” Parler, a large social media website, on its users’ information diets. Finally, the talk will discuss future research directions on improving online platforms, emphasizing the opportunities and challenges posed by the popularization of generative AI. Altogether, the work presented indicates that we can improve online platforms—and, by extension, our lives—if we rigorously investigate the causal effect of content curation practices.

Bio: Manoel Horta Ribeiro is a final year Ph.D. student in computer science at EPFL, Switzerland, advised by Professor Robert West. Previously, he received an MSc and BSc in computer science from UFMG, in Belo Horizonte, Brazil (where he was born and raised).

His research focuses on understanding the impact of content moderation, recommender systems, and monetization in online platforms from a computational perspective. His work has been covered in outlets from El País to NBC News, in think tanks like the ICCT, and has shaped products in companies like Meta and Reddit. He is a Meta Computational Social Science Fellow, a Forbes 30 under 30 awardee, and has received awards for his teaching (from EPFL) and his research (from ACM conferences and Altmetrics).

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This seminar is open to the public.

If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.

In-person attendance is open to Princeton University faculty, staff, students and alumni. This talk is open to the public via Zoom. 

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Decisions about policies when using widely-deployed technologies, including social media and more recently, generative AI, are often made in a centralized and top-down fashion. Yet these systems are used by millions of people, with a diverse set of preferences and norms. Who gets to decide what are the rules, and what should the procedures be for deciding them—and must we all abide by the same ones? This talk draws on theories and lessons from offline governance to reimagine how sociotechnical systems could be designed to provide greater agency and voice to everyday users and communities. This includes the design and development of: 1) personal moderation and curation controls that are usable and understandable to laypeople, 2) tools for authoring and carrying out governance to suit a community’s needs and values, and 3) decision-making workflows for large-scale democratic alignment that are legitimate and consistent.

Bio: Amy X. Zhang is an assistant professor at University of Washington's Allen School of Computer Science and Engineering, where she leads the Social Futures Lab, dedicated to reimagining social and collaborative systems to empower people and improve society. Her work has received awards at ACM CHI and ACM CSCW, and she has been a Google Research Scholar, a Belfer Fellow at the ADL, a Berkman Klein Fellow, a Google PhD Fellow, and an NSF CAREER recipient and Graduate Research Fellow.

Her work has been profiled in BBC’s Click television program, CBC radio, and featured in articles by ABC News, The Verge, New Scientist, and Poynter. Besides her work at UW, she is also a research consultant at AI2 on the Semantic Scholar team, and prior to UW, she was a Stanford postdoctoral researcher after completing a PhD at MIT CSAIL, where she received the George Sprowls Best Thesis Award at MIT in computer science. She received an MPhil in Computer Science at the University of Cambridge on a Gates Fellowship and a BS in Computer Science at Rutgers University, where she was captain of the Division I Women's tennis team.

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This talk will be recorded and posted to the CITP website, YouTube channel and to Media Central.

If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.

Rescheduled from Feb 13

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Using driverless cars, artificial intelligence, cochlear implants, nuclear power and other examples, this talk will walk through various hurdles to the successful legal analysis and governance of emerging technology. The talk will identify law’s unique role in channeling technology toward human flourishing, argue that law and technology constitutes a distinct field of study, and suggest a step-by-step methodology for this field going forward.

Bio: Ryan Calo is the Lane Powell and D. Wayne Gittinger Professor at the University of Washington School of Law. Calo is a visiting fellow at CITP for the spring 2024 semester. He is a founding co-director (with Batya Friedman and Tadayoshi Kohno) of the interdisciplinary UW Tech Policy Lab and a co-founder (with Chris Coward, Emma Spiro, Kate Starbird, and Jevin West) of the UW Center for an Informed Public. He holds a joint appointment at the Information School and an adjunct appointment at the Paul G. Allen School of Computer Science and Engineering.

Calo’s research on law and emerging technology appears in leading law reviews (California Law Review, Columbia Law Review, Duke Law Journal, UCLA Law Review, and University of Chicago Law Review) and technical publications (MIT Press, Nature, Artificial Intelligence) and is frequently referenced by the national media. His work has been translated into at least four languages. Calo has testified three times before the United States Senate and organized events on behalf of the National Science Foundation, the National Academy of Sciences, and the Obama White House. He has been a speaker at President Obama’s Frontiers Conference, the Aspen Ideas Festival, and NPR‘s Weekend in Washington.

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In-person attendance is restricted to Princeton University faculty, staff and students.

This talk is open to the public via Zoom.

This talk will be recorded and posted to the CITP website, YouTube channel and to Media Central.

If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.

Amid mounting legal and financial strife, mainstream platform companies—from Meta and YouTube to TikTok and Twitch–are doubling down on their idealistic framing of the digital “Creator Economy.” But the image of social media as an entrepreneurial Promised Land is belied by the precarious, even perilous realities of platform-dependent labor. Drawing upon insight from more than 80 interviews with digital content creators, influencers, and streamers, this talk illuminates the source of their plight: a platformed visibility bind. In a labor market where algorithms are key arbiters of success—and failure—creators struggle to defy the imminent threat of invisibility. But they must also navigate the risks of hypervisibility, from burnout and cultural appropriation to trolling and targeted harassment.

This talk argues that the consequences of this bind are amplified for marginalized creators—including women, people of color, and members of the LGBTQ+ community. While this research provides a necessary rejoinder to creator economy boosterism, it also lays bare creators’ efforts to resist and even subvert platforms’ governing visibility logics. Moreover, by shedding light on the contradictions inherent in the new realm of creative work, this research provides valuable lessons for anyone roused by the siren song to “put themselves out there.” 

Bio: Brooke Erin Duffy, Ph.D., is an associate professor in the Department of Communication at Cornell University, where she holds affiliate appointments in the Programs in Feminist, Gender, and Sexuality Studies (FGSS) and Media Studies. Her research interests include: platforms and cultural production; social media influencers and the creator economy; gender, identity, and inequality; and algorithms and promotional culture. Duffy is the author or editor of four books, including (Not) Getting Paid to Do What You Love: Gender and Aspirational Labor in the Social Media Economy (Yale University Press, 2017/2022)—which Wired named as one of the “Top Tech Books of 2017”—and Platforms and Cultural Production (Polity, 2022), with Thomas Poell and David Nieborg.

Duffy has published her research in such journals as Journal of Communication, New Media & Society, the International Journal of Communication, Critical Studies in Media Communication, the International Journal of Cultural Studies, Social Media + Society, and Information, Communication, and Society. In addition to her academic publications, she has disseminated her research to a broader audience through popular writing in The Atlantic, Vox, Salon, Business Insider, Wired, and Quartz, among others. Duffy holds a Ph.D. from the University of Pennsylvania’s Annenberg School for Communication. For more information, visit: www.brookeduffy.com 

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In-person attendance at CITP seminars is open to Princeton University faculty, staff and students.

The public can join via Zoom. This talk will be livestreamed and recorded. The recording will be posted to our website, YouTube and Media Central.

If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.

At the University of Washington, we have been studying online rumoring for more than a decade and have had a front row seat as misinformation—and its more nefarious cousin, disinformation—have grown and metastasized within social media platforms and the broader information ecosystem. This talk will draw from that body of work, featuring four case studies, to demonstrate some of the salient dimensions of online rumoring, misinformation, and disinformation, how they have evolved over time, and where they intersect with the design of sociotechnical systems, human psychology, and intentional manipulation. An emerging conceptualization of these phenomena through the lens of collective sensemaking, as an interaction between data (or evidence), interpretations, and frames will be presented . Finally, we will discuss some of the challenges that researchers in the field are facing—from data access issues to harassment and legal threats—as our work documenting the manipulation of online systems draws attention from the people and organizations that perpetrate and/or benefit from that manipulation.

Bio: Kate Starbird is an associate professor in the Department of Human Centered Design & Engineering (HCDE) and director of the Emerging Capacities of Mass Participation (emCOMP) Laboratory at the University of Washington. She is also adjunct faculty in the Paul G. Allen School of Computer Science & Engineering and the Information School, and a data science fellow at the eScience Institute.

She is a co-founder of the University of Washington’s Center for an Informed Public, which formed in 2019 around a shared mission of resisting strategic misinformation, promoting an informed society, and strengthening democratic discourse. Her research is situated within human-computer interaction (HCI) and the emerging field of crisis informatics — the study of how information-communication technologies (ICTs) are used during crisis events. Her research examines how people use social media to seek, share, and make sense of information after natural disasters (such as earthquakes and hurricanes) and man-made crisis events (such as acts of terrorism and mass shooting events). More recently, her work has shifted to focus on the spread of disinformation in this context. Starbird’s research touches on broader questions about the intersection of technology and society—including the vast potential for online social platforms to empower people to work together to solve problems, as well as salient concerns related to abuse and manipulation of and through these platforms and the consequent erosion of trust in information.

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This virtual talk is open to Princeton University faculty, staff and students via Zoom.

This talk will not be recorded.

If you need an accommodation for a disability please contact Jean Butcher at butcher@princeton.edu at least one week before the event.