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Explainable AI for Decision-Making Applications
4.8
(3 ratings)
10 Days
·Cohort-based Course
Learn to build accurate, transparent, understandable ML models. Get real-world policy and compliance insights for high-risk applications.
Class is in session
Explainable AI for Decision-Making Applications
4.8
(3 ratings)
10 Days
·Cohort-based Course
Learn to build accurate, transparent, understandable ML models. Get real-world policy and compliance insights for high-risk applications.
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Course overview
Learn to build accurate, transparent, and interpretable ML/AI models.
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 explainable AI (XAI):
• The fANOVA framework
• From penalized GLM to EBM and GAMinet
• Monotonic GBMs
• Surrogate model approaches
• Explainable feature engineering
• 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.
Who is this course for
01
ML engineers and data scientists who want to learn about XAI.
02
Technical risk executives seeking an updated overview of newer ML approaches suited for high-risk applications.
03
Policy professionals interested in the current state of ML technologies that may be used to comply with regulations and standards.
Learning Objectives
Build accurate, transparent, and understandable ML models.
- Ecosystem of cutting-edge explainable ML models
- Essential compliance awareness for ML transparency
- Emergent audit and transparency laws and regulations
Interpret and explain sophisticated ML algorithms, enabling them to uncover insights, identify biases, and understand complex decisions.
- Best practices for post-hoc explanation
- Explainable feature engineering
- Testing explanation quality
Build safer and more trustworthy AI systems.
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.
This course includes
4 interactive live sessions
Lifetime access to course materials
In-depth lessons
Direct access to instructor
Projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Explainable AI for Decision-Making Applications
Course syllabus
Week 1
Apr 30—May 5
Week dates are set to instructor's time zoneEvents
- Apr
30
Session 1: Introduction to Explainable ML in Context
Session 1: Introduction to Explainable ML in Context
Tue, Apr 30, 2:00 PM - 3:30 PM UTC
- May
2
Session 2: The Ecosystem of Explainable Models
Session 2: The Ecosystem of Explainable Models
Thu, May 2, 2:00 PM - 3:30 PM UTC
Modules
- The Socio-technical Systems ApproachThe Socio-technical Systems Approach0 Lessons • 0 Projects
- The Data-scientific Method vs. The Scientific MethodThe Data-scientific Method vs. The Scientific Method0 Lessons • 0 Projects
- Transparency and Other AI ConsiderationsTransparency and Other AI Considerations0 Lessons • 0 Projects
- Transparency RequirementsTransparency Requirements0 Lessons • 0 Projects
- Inherent (and Wicked) DifficultiesInherent (and Wicked) Difficulties0 Lessons • 0 Projects
- Making ProgressMaking Progress0 Lessons • 0 Projects
- Explainable Feature EngineeringExplainable Feature Engineering0 Lessons • 0 Projects
- fANOVA: From Penalized GLM to EBM and GAMinetfANOVA: From Penalized GLM to EBM and GAMinet0 Lessons • 0 Projects
- Monotonic GBMsMonotonic GBMs0 Lessons • 0 Projects
- Explainable ML Demo 1Explainable ML Demo 10 Lessons • 0 Projects
- DiscussionDiscussion0 Lessons • 0 Projects
Week 2
May 6—May 9
Week dates are set to instructor's time zoneEvents
- May
7
Session 3: Post-hoc Explanation
Session 3: Post-hoc Explanation
Tue, May 7, 2:00 PM - 3:30 PM UTC
- May
9
Session 4: Emergent Audit and Transparency Laws and Regulations
Session 4: Emergent Audit and Transparency Laws and Regulations
Thu, May 9, 2:00 PM - 3:30 PM UTC
Modules
- Surrogate Model ApproachesSurrogate Model Approaches0 Lessons • 0 Projects
- Plots of Model Behavior: Partial Dependence, ICE, and ALEPlots of Model Behavior: Partial Dependence, ICE, and ALE0 Lessons • 0 Projects
- Prototypes and CriticismsPrototypes and Criticisms0 Lessons • 0 Projects
- Feature Importance: LOCO, LOFO, and PerturbationFeature Importance: LOCO, LOFO, and Perturbation0 Lessons • 0 Projects
- SHAP: The Good, Bad, and UglySHAP: The Good, Bad, and Ugly0 Lessons • 0 Projects
- Security and Bias ConsiderationsSecurity and Bias Considerations0 Lessons • 0 Projects
- Testing Explanation QualityTesting Explanation Quality0 Lessons • 0 Projects
- Adverse Action NoticesAdverse Action Notices0 Lessons • 0 Projects
- NYC Local Law 144NYC Local Law 1440 Lessons • 0 Projects
- EU AI ActEU AI Act0 Lessons • 0 Projects
- NIST AI Risk Management FrameworkNIST AI Risk Management Framework0 Lessons • 0 Projects
- Explainable ML Demo 2Explainable ML Demo 20 Lessons • 0 Projects
- DiscussionDiscussion0 Lessons • 0 Projects
Meet Patrick Hall
Patrick Hall
Principal Scientist, HallResearch.ai & Assistant Professor, George Washington University
Patrick Hall is the principal scientist at HallResearch.ai. He is also an assistant professor of decision sciences at the George Washington University School of Business, teaching data ethics, business analytics, and machine learning classes. Patrick conducts research in support of NIST's AI Risk Management Framework, works with leading fair lending and AI risk management advisory firms, and serves on the board of directors for the AI Incident Database. Prior to co-founding HallResearch.ai, Patrick was a partner at BNH.AI, where he pioneered the emergent discipline of auditing and red-teaming generative AI systems; he also led H2O.ai's efforts in the development of responsible AI, resulting in one of the world's first commercial applications for explainability and bias mitigation in machine learning. Patrick started his career in global customer-facing roles and R&D roles at SAS Institute.
Patrick studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University. He has been invited to speak on AI and machine learning topics at the National Academies of Sciences, Engineering, and Medicine, the Association for Computing Machinery SIG-KDD (“KDD”), and the American Statistical Association Joint Statistical Meetings. He has been published in outlets like Information, Frontiers in AI, McKinsey.com, O'Reilly Media, and Thomson Reuters Regulatory Intelligence, and his technical work has been profiled in Fortune, WIRED, InfoWorld, TechCrunch, and others. Patrick is the lead author of the book Machine Learning for High-Risk Applications.
With affiliations across private industry, civil society, academia, and government, Patrick brings one of the widest possible perspectives to AI and matters of risk. He has built machine learning software solutions and advised on AI risk for Fortune 100 companies, cutting-edge startups, Big Law, and US and foreign government agencies.
Course Schedule
4-6 hours per week
Tuesday, April 30, 2024
10:00 AM - 11:30 AM ET
Live lectures followed by discussion session.
Thursday, May 2, 2024
10:00 AM - 11:30 AM ET
Live lectures and demos followed by discussion session.
Tuesday, May 7, 2024
10:00 AM - 11:30 AM ET
Live lectures and demos followed by discussion session.
Thursday, May 9, 2024
10:00 AM - 11:30 AM ET
Live lectures followed by discussion session.
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Explainable AI for Decision-Making Applications
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Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you