Cohort 1

Learn to build accurate, transparent, understandable ML models. Get real-world policy and compliance insights for high-risk applications.

Patrick Hall

Principal Scientist, HallResearch.ai                                               
 & Assistant Professor, George Washington University

Explainable AI for Decision-Making Applications

The National Academies of Sciences, Engineering, and Medicine

National Institute of Standards and Technology

American Bar Association

MDPI

SP Global

In today's rapidly evolving artificial intelligence landscape, engineers and risk managers play pivotal roles in ensuring innovation and accountability. Our course on explainable AI (XAI) equips professionals with essential skills to navigate this rapidly expanding complex terrain. Students will gain hands-on experience exploring compliance considerations derived from real-world applications across diverse sectors. By merging technical expertise with compliance insights, this course empowers individuals to drive impactful, transparent AI solutions in their respective domains, fostering innovation and integrity. This course explores XAI, covering essential background definitions and concepts, explainable feature engineering, the diverse ecosystem of XAI models, post-hoc explanation methods, and the latest developments in audit and transparency laws and regulations. Some featured topics in this course on&nbsp;explainable AI (XAI): • The&nbsp;fANOVA&nbsp;framework• From&nbsp;penalized GLM to EBM and GAMinet•&nbsp;Monotonic GBMs•&nbsp;Surrogate model approaches•&nbsp;Explainable feature engineering•&nbsp;LOCO, LOFO, and pertubation• SHAP: The good, bad, and ugly• Security and bias considerations Background information and definitions are sourced from the National Institute for Standards and Technology (NIST), the National Academies, and other authoritative sources. The instructor teaches technical subjects with Python examples and compliance lessons based on his real-world experience in consumer finance, employment, and other regulated applications of ML. (Note: The instructor is not an attorney.) This course is designed primarily for data scientists and ML engineers. Technical risk executives or risk managers may find this course helpful in providing an updated overview of newer ML approaches suited for high-stakes applications. This course may interest others too. Materials may help regulators or policy professionals gain insights into the current state of ML technologies that can be used to comply with laws, regulations, or standards. If you're coming to ML from physics, econometrics, or psychometrics, this course can help you learn how to blend newer ML techniques with established domain expertise and notions of validity or causality.

Learn to build accurate, transparent, and interpretable ML/AI models. 

ML engineers and data scientists who want to learn about XAI.

Technical risk executives seeking an updated overview of newer ML approaches suited for high-risk applications.

Policy professionals interested in the current state of ML technologies that may be used to comply with regulations and standards.

Who is this course for

Learning Objectives

<ul><li>Ecosystem of cutting-edge explainable ML models</li><li>Essential compliance awareness for ML transparency</li><li>Emergent audit and transparency laws and regulations</li></ul>

Build accurate, transparent, and understandable ML models.

<ul><li>Best practices for post-hoc explanation</li><li>Explainable feature engineering</li><li>Testing explanation quality</li></ul>

Interpret and explain sophisticated ML algorithms, enabling them to uncover insights, identify biases, and understand complex decisions.

Be equipped to build safer and more trustworthy AI systems, tackle real-world problems confidently, and drive positive impact in various domains such as healthcare, finance, and human resources.

Build safer and more trustworthy AI systems.

Provide a brief description for each module as a way to sell the value of what students will be learning throughout the course.

First Module

Second Module

Third Module

Course syllabus

Meet Patrick Hall

Live lectures followed by discussion session.

Monday, July 8, 2024

Live lectures and demos followed by discussion session.

Wednesday, July 10, 2024

Monday, July 15, 2024

Wednesday, July 17, 2024

Course Schedule

This course builds on live workshops and hands-on projects

Active hands-on learning

You’ll be interacting with other learners through breakout rooms and project teams

Interactive and project-based

Join a community of like-minded people who want to learn and grow alongside you

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About this course

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