A SoDa | ASA Symposium: In Celebration of Privacy Week, a conversation with Prof. Dr. Ivan Habernal and Prof. Rochelle E. Tractenberg
Co-Sponsored by the American Statistical Association and the UMD Social Data Science Center
Two Presentations with Q&A
Tuesday, January 28, 2025
12:00 pm – 1:00 pm EST (Online)
REGISTER HERE
People, Language, Large Language Models, and Privacy: A Technical Problem or a Fundamental Puzzle?
Abstract:
In this talk, I will most likely ask more questions than give answers. We know that people have a fundamental right to privacy, and we know that differential privacy gives us formal guarantees to protect the privacy of people in a database and to do machine learning. But humans also communicate through language, we use written text to do natural language processing, and we train large language models using the entire internet. So how does this work with privacy? From a technical perspective, it looks like it’s just about finding faster techniques or better models – I’m going to talk here about rewriting texts and generating synthetic data under differential privacy. But from a privacy perspective, things quickly get messy when we start asking fundamental questions, and we might be faced with the conundrum: What is privacy in language in the first place?
Presented by:
Prof. Dr. Ivan Habernal
Head of the Trustworthy Human Language Technologies group
Full Professor
Research Center Trustworthy Data Science and Security and
Faculty of Computer Science, Ruhr-Universität Bochum
Web: https://www.trusthlt.org
Social: https://twitter.com/ivanhabern
https://sigmoid.social/@habern
From Dialogue to Data: How Statisticians Can Safeguard Privacy and Promote Trust in LLMs
Abstract:
Generative AI and large language models are unleashing unprecedented capabilities in data processing, but they also raise serious questions about protecting the individuals and organizations whose data fuel these models. In this talk, I will bridge ethical frameworks from statistics, computing, and mathematics to illustrate how statisticians can help ensure privacy is more than a buzzword. As large language models (LLMs) blur the boundaries between data, dialogue, and personal expression, statisticians play a pivotal role in shaping responsible AI practices. This talk explores how principles from statistics, computing, and mathematics can help safeguard privacy in language-based systems and cultivate (deeper) public trust. Rather than prescribing a one-size-fits-all formula, we will reflect on the key question: How can we balance transparency, accountability, and intellectual freedom in an environment where AI may inadvertently expose sensitive information? Drawing on ethical frameworks and real-world examples, we’ll highlight the potential for statisticians to steer LLM developments in ways that honor human autonomy, encourage fairness, and uphold the spirit of confidentiality—ultimately fostering more trustworthy AI. By combining methodological rigor with ethical deliberation, statisticians have a unique role to play in defending privacy norms, cultivating transparency, and embedding responsible AI practices across research and industry.
Presented by:
Prof. Rochelle E. Tractenberg
Director, Collaborative for Research on Outcomes and -Metrics Co-Director, National Capital Spinal Cord Injury Model System (NC-SCIMS)
Professor, Departments of Neurology; Biostatistics, Bioinformatics & Biomathematics; and Rehabilitation Medicine
Georgetown University
Web: https://
https://gufaculty360.
The one-hour webinar will include a presentation and a Q&A moderated by Frauke Kreuter.
REGISTER HERE
Co-sponsored by
The American Statistical Association
Our Mission: Promoting the Practice and Profession of Statistics®
Our Vision: A world that relies on data and statistical thinking to drive discovery and inform decisions
The American Statistical Association is the world’s largest community of statisticians, the “Big Tent for Statistics.” It is the second oldest, continuously operating professional association in the country. Since it was founded in Boston in 1839, the ASA has supported excellence in the development, application, and dissemination of statistical science.
Our members serve in industry, government, and academia in more than 90 countries, advancing research and promoting sound statistical practice to inform public policy and improve human welfare.
