Abstract: As organizations rush to innovate and modernize business with AI, one thing is clear — it's critical to monitor your AI models for performance and accuracy, preventing bias, and maintaining explainability. Attend this session to learn how Watson OpenScale helps enterprises bring transparency and audit-ability to AI-infused applications by highlighting possible fairness issues, and generating explanations for individual transactions, including the attributes that were used to make the prediction.
Bio: I'm a Data Scientist/Machine Learning Engineer with IBM Data Science Elite team, based in San Francisco. After studying my PhD in the field of Computer Engineering at the Center for Pattern Analysis and Machine Intelligence at the University of Waterloo, I started my journey as Big data & Analytics consultant, I then joined IBM as open source solution engineer and was working for IBM Canada for 2 years where I was honored to be awarded “the best of IBM” in 2018. I recently moved from Toronto to San Francisco and joined DSE team. I really enjoy being on the team as I'm learning how to apply Data Science in real world applications and get the chance to meet with different customers in different industries and help them in their Data Science journey. Also, I enjoy working with the top talented people in the team and have a chance to learn from them.