Dear all, The next talk in the IARCS Verification Seminar Series will be given by Shahaf Bassan, a senior PhD student in the Katz Lab at the Hebrew University of Jerusalem specializing in explainable AI. The talk is scheduled on Thursday, June 12, at 1900 hrs IST (add to Google calendar <https://calendar.google.com/calendar/event?action=TEMPLATE&tmeid=NTRxaDRxdGliZGJyZTZrdjhmZ25sbHFzNXUgdnNzLmlhcmNzQG0&tmsrc=vss.iarcs%40gmail.com> ). The details of the talk can be found on our webpage ( https://fmindia.cmi.ac.in/vss/), and also appended to the body of this email. The Verification Seminar Series, an initiative by the Indian Association for Research in Computing Science (IARCS), is a monthly, online talk-series, broadly in the area of Formal Methods and Programming Languages, with applications in Verification and Synthesis. The aim of this talk-series is to provide a platform for Formal Methods researchers to interact regularly. In addition, we hope that it will make it easier for researchers to explore newer problems/areas and collaborate on them, and for younger researchers to start working in these areas. All are welcome to join. Best regards, Akash, Deepak, Madhukar, Srivathsan ============================================================= Title: “Formal XAI”: Can we formally explain ML models? Meeting Link: https://us02web.zoom.us/j/89164094870?pwd=eUFNRWp0bHYxRVpwVVNoVUdHU0djQT09 (Meeting ID: 891 6409 4870, Passcode: 082194) Abstract: The goal of explainability is to make sense of the decisions made by black-box ML models. Unfortunately, many existing explanation methods are heuristic, which makes them unreliable. In this talk, I will present our work on developing techniques that provide explanations with formal guarantees, ensuring their trustworthiness. These techniques often rely on formal verification, particularly neural network verification tools. In addition, we examine these explanations from a theoretical perspective - studying the computational challenges they pose and exploring ways to build practical tools that address these challenges and enable the generation of reliable explanations for ML models. Bio: Shahaf Bassan is a senior PhD student in the Katz Lab at the Hebrew University of Jerusalem specializing in explainable AI. His research focuses on developing explanation techniques with formally provable guarantees, at the intersection of explainability, formal verification, and ML theory. His work spans both theoretical foundations and practical applications. Sahaf has presented his research at leading conferences in formal verification (e.g., TACAS) and machine learning (e.g., ICML, ICLR). His research goal is to enhance trust in ML models by providing trustworthy, verifiable explanations.