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Hanbyul Lee’s MS in Statistics Project Presentation

Apr. 25 @ 12 p.m. - 2 p.m.

Free

Title: An Introduction to Interpretable and Explainable AI

Abstract:  As complex artificial intelligence (AI) technologies rapidly evolve, interpretability and explainability have come to play an important role in the prediction and decision-making processes of AI models. In critical decision-making domains such as finance, healthcare, and law, where high levels of accountability and trust are required, it is becoming essential to provide an explanation for why a particular AI model predicts a certain outcome or categorizes data in a certain way.

This project introduces the core ideas and techniques related to interpretable machine learning (IML) and explainable AI (XAI). We begin with a model-specific explanation of the fundamental concepts involved in regression models, IML’s flagship model, and the interpretation of their results, and then go into detail about SHapley Additive exPlanations (SHAP), a model-agnostic XAI method that is universally applicable to nonlinear machine learning models and deep learning.

Details

Date:
Apr. 25
Time:
12 p.m. - 2 p.m.
Cost:
Free
Event Type:
School/College:
College of Liberal Arts and Sciences
Department:
Mathematical and Statistical Sciences