GW School of Business Appoints Its First Chief AI Officer
Patrick Hall
Patrick Hall
Teaching Assistant Professor of Decision Sciences
Chief AI Officer, GW School of Business
Contact:
2201 G Street NW
Washington, D.C. 20052
Patrick Hall is a teaching assistant professor of decision sciences and Chief AI Officer at the George Washington University School of Business, teaching data ethics, business analytics, and machine learning classes. He also conducts research in support of NIST's AI risk management framework, is affiliated with leading fair lending and AI risk management advisory firms, and serves on the board of directors for the AI Incident Database.
Patrick has co-founded several services firms where he pioneered the emergent discipline of auditing and red-teaming AI systems; he also led H2O.ai's efforts in the development of responsible AI products, resulting in one of the world's first commercial applications for explainability and bias management in machine learning.
Patrick has been invited to speak on AI and machine learning topics at the National Academies, the Association for Computing Machinery SIG-KDD Conference ("KDD"), and the American Statistical Association Joint Statistical Meetings. His expertise has been sought in the New York Times and NPR, 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.
- "Artificial intelligence risk management framework: Generative artificial intelligence profile." NIST Trustworthy and Responsible AI Gaithersburg, MD, USA (2024).
- Hall, Patrick, Benjamin Cox, Steven Dickerson, Arjun Ravi Kannan, Raghu Kulkarni, and Nicholas Schmidt. "A United States fair lending perspective on machine learning." Frontiers in Artificial Intelligence 4 (2021): 695301.
- Hall, Patrick, James Curtis, and Parul Pandey. Machine Learning for High-risk Applications: Approaches to Responsible AI. "O'Reilly Media, Inc.", 2023.
- Schwartz, Reva, Apostol Vassilev, Kristen Greene, Lori Perine, Andrew Burt, and Patrick Hall. "Towards a Standard for Identifying and Managing Bias in Artificial Intelligence." (2022).
- Gill, Navdeep, Patrick Hall, Kim Montgomery, and Nicholas Schmidt. "A responsible machine learning workflow with focus on interpretable models, post-hoc explanation, and discrimination testing." Information 11, no. 3 (2020): 137.
- DNSC 6290: Practical AI
- DNSC 6330: Introduction to Responsible Machine Learning
- DNSC 6317: MSBA Practicum
- DNSC: 4289: BSBA Capstone
- DNSC 3288: Big Data Ethics and Analytics Edge
Patrick's academic and applied work are focused in the following areas:
- AI Governance: Expertise in designing and assessing AI risk management frameworks, regulatory compliance, and model governance practices for high-stakes applications.
- Responsible AI: Implementing fairness, transparency, explainability, security, and harm reduction in machine learning systems; contributor to public-sector and academic guidelines for ethical AI.
- Applied Machine Learning and AI Evaluation: Deep experience in developing and evaluating machine learning systems with an emphasis on interpretability, robustness, and real-world performance.
- Consulting Practice: Co-founder of BNH.AI, HallResearch.ai, and advisor to numerous organizations on deploying safe and effective AI systems across sectors such as finance, healthcare, and legal tech.
• 2025 Department of Commerce Gold Medal (ITL Associates Reflection)
• 2024 American Banker AI 100 List
• MSBA Outstanding Faculty Award, 2023, 2022, and 2019