Amy Hodler

Amy Hodler

Founder, Consultant at GraphGeeks.org

    Amy Hodler is an evangelist for graph analytics and responsible AI. She’s the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the Routledge book, Massive Graph Analytics and Bloomsbury book, AI on Trial. Amy has decades of experience in emerging tech at companies such as Microsoft, Hewlett-Packard (HP), Hitachi IoT, Neo4j, Cray, and RelationalAI. Amy is the founder of GraphGeeks.org promoting connections everywhere.

    All Sessions by Amy Hodler

    Day 2 04/24/2024
    1:40 pm - 2:40 pm

    Graphs: The Next Frontier of GenAI Explainability

    <span class="etn-schedule-location"> <span class="firstfocus">Generative AI</span>

    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.

    Day 2 04/24/2024
    1:40 pm - 2:40 pm

    Graphs: The Next Frontier of GenAI Explainability

    <span class="etn-schedule-location"> <span class="firstfocus">Generative AI</span> </span>

    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.

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