Director of Research (if dissertation) or Advisor (if thesis)
Forsyth, David
Doctoral Committee Chair(s)
Forsyth, David
Committee Member(s)
Li, Bo
Lazebnik, Svetlana
Krueger, David
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Ai Safety
Ai Risk
Robustness
Red Teaming
Neural Trojans
Trojan Detection
Alignment
Model Stealing
Language
eng
Abstract
Artificial intelligence (AI) has rapidly improved over the past decade, leading to widespread adoption of AI systems and demonstrating the potential for AI to greatly benefit society. However, as with any powerful new technology, AI introduces risks that must be managed to fully realize these benefits. Recent breakthroughs in the generality of AI systems have drawn increased attention to AI risks, including those of a potentially catastrophic nature. To help manage these anticipated risks, we take a defense in depth approach, combining different areas of AI safety research to address different aspects of AI risk. We present research on making AI systems more robust to adversarial influence, monitoring AIs for hidden behavior and trojans, enabling AIs to understand and adhere to human values, and finally addressing systemic problems to enable increased transparency.
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