Addressing the High Failure Rate of AI & ML Projects - Examples From the Field
Despite the rapid evolution of AI, projects still fail at a disappointingly high rate. In the past, capturing data at scale and building models was the challenge, but today we're confronted with the issue of making AI more robust while avoiding the risk of unintended consequences. While the tools are new, many challenges remain the same. In this talk, I will share real-world stories and applied examples that demonstrate:
- How to build the business case for an AI project (and get buy-in)
- Navigating AI project management to prevent failure
- How to mitigate the risks of unintended consequences from using AI
Our presenter this month is Cal Al-Dhubaib. Cal is a data scientist and entrepreneur, with an emphasis on designing and building trustworthy AI. He empowers organizations to launch data science initiatives that grow the bottom line. He is especially passionate about the ethics of AI and how organizations can build the right talent to support AI initiatives.
Cal has received awards at both the national and international levels for his work in predictive modeling and entrepreneurship and has been listed on Crain's Cleveland 20 in their 20s and Cleveland Smart 50.