Machine learning solutions are revolutionising AI, but their instability against adversarial examples – small perturbations to inputs that can catastrophically affect the output – raises concerns about the readiness of this technology for widespread deployment. Using illustrative examples, this lecture will give an overview of techniques being developed to improve the robustness, safety and trust in AI systems.
About the speaker
Marta Kwiatkowska is Professor of Computing Systems and Fellow of Trinity College, University of Oxford. She is known for fundamental contributions to the theory and practice of model checking for probabilistic systems, and is currently focusing on safety, robustness and fairness of automated decision making in Artificial Intelligence. She led the development of the PRISM model checker (www.prismmodelchecker.org), which has been adopted in diverse fields, including wireless networks, security, robotics, healthcare and DNA computing, with genuine flaws found and corrected in real-world protocols. Her research has been supported by two ERC Advanced Grants, VERIWARE and FUN2MODEL, EPSRC Programme Grant on Mobile Autonomy and EPSRC Prosperity Partnership FAIR. Kwiatkowska won the Royal Society Milner Award, the BCS Lovelace Medal and the Van Wijngaarden Award, and received an honorary doctorate from KTH Royal Institute of Technology in Stockholm. She is a Fellow of the Royal Society, Fellow of ACM and Member of Academia Europea.
Prof. Kwiatkowska's Google Scholar page
Technical sessions proposal submission: November 14, 2022
- Paper submission (no extensions): May 23, 2023
- Position paper submission: June 7, 2023
- Author notification: July 11, 2023
- Final paper submission, registration: July 31, 2023
- Discounted payment: August 15, 2023
- Conference date: September 17–20, 2023