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DTSTART;TZID=America/Chicago:20260529T130000
DTEND;TZID=America/Chicago:20260529T150000
DTSTAMP:20260530T073623Z
SUMMARY:Chenhao Zhang CS PhD Final Defense: Regulation of Algorithms with Online Learning\, Information Flow Control\, and Data Privacy
UID:642615@northwestern.edu
TZID:America/Chicago
DESCRIPTION:While the ubiquitous adoption of algorithms in decision-making and content generation greatly improves societal efficiency\, it has also raised various regulatory concerns\, including antitrust\, nondiscrimination\, and intellectual property protection.   This dissertation investigates techniques from three different computer science research areas to support the regulation of algorithms: First\, based on ideas from online learning theory\, we propose a framework for the regulation of algorithmic collusion by auditing from data. Second\, by adapting principles from information flow control with dynamic policies\, we design a type system to reason about iteration (probabilistic) independence to support the regulation of the fairness of classification algorithms. Third\, we study a proper data attribution notion informed by data privacy concepts for the regulation of credit attribution of generative models. These results demonstrate that the foundations of the regulation of algorithms can benefit from techniques from these distinct areas of computer science research and point to future research directions.\n\nWebcast Link: https://northwestern.zoom.us/j/96729832807
LOCATION:Mudd Hall ( formerly Seeley G. Mudd Library)\, Mudd 3514\, 2233 Tech Drive\, Evanston\, IL 60208
TRANSP:OPAQUE
URL:https://planitpurple.northwestern.edu/event/642615
CREATED:20260522T050000Z
STATUS:CONFIRMED
LAST-MODIFIED:20260522T050000Z
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