Michelle Yi

Michelle Yi

Board Member at Women In Data

Michelle Yi is a technology leader that specializes in machine learning and cloud computing. She has 15 years of experience in the technology industry, contributed to the original IBM Watson showcased on Jeopardy, and enjoys building and leading teams that develop and deploy AI solutions to solve real-world problems. Michelle is passionate about diversity, STEM education/careers for our minority communities, and serves both on the board of Women in Data and as an avid volunteer for Girls Who Code.

All Sessions by Michelle Yi

Graphs: The Next Frontier of GenAI Explainability

Generative AI | Intermediate

In a world obsessed with making predictions and generative AI, we often overlook the crucial task of making sense of these predictions and understanding results. If we have no understanding of how and why recommendations are made, if we can’t explain predictions – we can’t trust our resulting decisions and policies. In the realm of predictions, explainability, and causality, graphs have emerged as a powerful model that has recently yielded remarkable breakthroughs. Graphs are purposefully designed to capture and represent the intricate connections between entities, offering a comprehensive framework for understanding complex systems. Leading teams use this framework today to surface directional patterns, compute complex logic, and as a basis for causal inference. This talk will examine the implications of incorporating graphs into the realm of generative AI, exploring the potential for even greater advancements. Learn about foundational concepts such as directed acrylic graphs (DAGs), Jedeau Pearl’s “do” operator, and keeping domain expertise in the loop. You’ll hear how the explainability landscape is evolving, comparisons of graph-based models to other methods, and how we can evaluate the different fairness models available. We’ll look into the open source PyWhy project for causal inference and the DoWhy method for modeling a problem as a causal graph with industry examples. By identifying the assumptions and constraints up front as a graph and applying that through each phase of modeling mechanisms, identifying targets, estimating causal effects, and refuting these with each inference – we can improve the validity of our predictions. We’ll also explore other open source packages that use graphs for counterfactual approaches, such as GeCo and Omega. Join us as we unravel the transformative potential of graphs and their impact on predictive modeling, explainability, and causality in the era of generative AI.

Open Data Science

 

 

 

Open Data Science
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Cambridge, MA 02142
info@odsc.com

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