Hi, I'm Lisha. I work on AI fairness at the MIT at VMBatson and L.A.L. in Cambridge. And here are two answers to three questions about IBM's work in the AI fairness. Each day, AI introduces new benefits for business and society, but this powerful technology must be researched, trained, and deployed it to the world responsibly. At IBM, we are continuing to research and develop tools and methods that can create more trust-worthy and responsible AI systems. One example is our new Python library, Inferness, which is designed to train and audit machine-learning models for individual fairness. It is also the first open-source library for individual fairness. We want to help developers and data scientists to start incorporating fairness into their workflows. So we are also creating fairness with vulnerability tools and releasing demos and tutorials to demystify AI fairness. In the next row, my colleague Sumia will share another new technique and deraille at the lab. It is designed to reduce bias in AI systems. Please check out our Inferness 2 km on GitHub. Thank you.