Open Source Summit + AI_dev Japan 2024

Due to the ever-increasing adoption of AI into the lives of daily users, trustworthy AI is of utmost priority. Even though advocates of AI globally have started talking about ethical considerations during ML model building, in reality, very few people know how to create robust, privacy-preserving, and fair AI models. In this talk, I'll explore 2 concrete technical concepts of trustworthy AI, namely ensuring robustness and fairness in ML models. Robustness: 1. Attendees will go through an in-depth understanding of critical vulnerabilities of common AI models and how to exploit them to adversarially attack the model (e.g., inference attacks, data poisoning). 2. This will be followed by simple defence strategies to increase robustness (e.g., gradient obfuscation, transformations). 3. This will be further followed by adaptive attacks on previous defence strategies thereby motivating the concept of certified robustness of AI models. Fairness: 1. Attendees will get to know how they can unconsciously encode bias (representational bias, model bias, etc) during training AI models. 2. This is followed by strategies to correct this bias using domain knowledge to create fair AI models.