Abstract: 

Deepfake photos and videos are already impacting many industries and sectors of society, in both positive and negative ways. In this session I'll weave between the social context of deepfakes (how they've been used and what impact they've had) and the technical side of them (how they're made, and some approaches to detecting them). This is the multifaceted story of deepfakes. No technical background is needed---the discussion of how they're made and detected will be done at a broad overview level focusing on the concepts, with a brief tour at the end of more specific resources for those interested in digging in deeper and exploring some relevant Python tools.

Session Outline:

"Module 1: A conceptual view of how deepfake photos and videos are made (explaining the machine learning involved, but not requiring ML background beyond the most basic idea of supervised and unsupervised learning)

Module 2: The societal impact of deepfake photos and videos (a non-technical tour of the good and bad uses)

Module 3: What approaches are there to detecting deepfakes, and how effective are they? (This module provides a gentle conceptual overview then briefly tours some resources for those who wish to dig in deeper after the tutorial.)"

Background Knowledge:

Basic familiarity with supervised and unsupervised learning is useful but not strictly required

Bio: 

Noah Giansiracusa (PhD in math from Brown University) is a tenured associate professor of mathematics and data science at Bentley University, a business school near Boston. His research interests range from algebraic geometry to machine learning to empirical legal studies. After publishing the book How Algorithms Create and Prevent Fake News in July 2021, Noah has gotten more involved in public writing and policy discussions concerning data-driven algorithms and their role in society. He's written op-eds for Barron's, Boston Globe, Wired, Slate, and Fast Company and is currently working on a second book, Robin Hood Math: How to Fight Back When the World Treats You Like a Number, with a Foreword by Nobel Prize-winning economist Paul Romer.