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DataBytes: Causal AI: The Key to High-Stakes Decision Making
December 5 @ 4:00 pm - 5:00 pm
The National Consortium for Data Science looks forward to welcoming back Christopher Lam, CEO of Epistamai on December 5th for our next DataBytes event as he discusses Causal AI: The Key to High-Stakes Decision Making.
There has been tremendous attention to the generative AI wave and its enormous potential to transform industries. But there is a hidden wave developing right behind it called causal AI.
Whereas generative AI is optimized for low-stakes decisions like chatbots and image generation, it is not designed to address issues like ethics or trustworthiness that are essential for using AI in high-stakes decisions like credit and hiring decisions. This is where causal AI fits into the picture.
In this presentation, Christopher Lam will discuss how to use causal AI to build AI systems that society can trust for high-stakes decision making. Lam will show how causal AI can help bridge the gap between symbolic AI and machine learning, demonstrating the value of integrating human knowledge and reasoning about the world to improve how data is analyzed. He will demonstrate through a use case how this more human-centric approach to AI can be used to build fairer and more equitable AI systems that are aligned with society’s democratic values. Finally, he will describe a new causal hierarchy, one that integrates machine learning with causal inference and system dynamics.
There’s a fundamental weakness in how AI systems are being built today, which is due to an overreliance on machine learning and correlation. By strengthening the foundations of AI with causality, we can get a step closer towards developing a grand unified theory of AI. Such a theory is essential for building artificial general intelligence.
About the Speaker – Christopher Lam
Christopher Lam is the founder and CEO of Epistamai, an AI research company based in the Research Triangle that is focused on understanding AI ethics through the lens of causality. The inspiration for his startup came from his work at the Federal Reserve, where he did research on algorithmic bias in credit decisions. He is an evangelist for the emerging field of causal data science, which could help us to solve intractable problems in data science today.