Improving the fairness and reliability of AI solutions | Global AI Developer Days

AI is at the heart of technological innovations. However, it is vital that we build solutions that are more fair, trustworthy, transparent, and less harmful. This does not just have an impact on our society, but on the credibility of organizations the build or use AI as well. In this session will cover some of the best practices of debugging models through error analysis, fairness assessment, model behavior explainability and counterfactuals/what-if analysis. In addition, we will illustrate how Azure ML service simplifies how data scientists and developers can improve AI models through its easy-to-use Responsible AI (RAI) dashboard which is built on some of the best open-source tools such as Fairlearn, DICE, InterpreML, EconML etc. Ruth Yakubu Principle Cloud Advocate at Microsoft Ruth Yakubu is a Principal Cloud Advocate at Microsoft. Ruth specializes in Java, Advanced Analytics, Data Platforms and Artificial Intelligence (AI). In addition, she's been a tech speaker at several conferences like Microsoft Ignite, O'reilly velocity, Devoxx UK, Grace Hopper Dublin, TechSummit, Websummit and numerous other developer conferences. Prior to Microsoft, she has also worked for great companies like UNISYS, ACCENTURE and DIRECTV over the years where she gained a lot of experience with software architectural design and programming. She’s awarded Dzone.com’s Most Valued Blogger.