AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning
Paper Accepted for Presentation at the 2018 Armed Forces Communications and Electronics Association (AFCEA) C4I and Cyber Conference, Erie Canal Chapter, New York, June 19 & 20, 2018.
7 Pages Posted: 16 Jul 2018
Date Written: May 16, 2018
The current paper proposes how model risk management in operationalizing machine learning for algorithm deployment can be applied in national C4I and Cyber projects such as Project Maven. It builds upon recent leadership of global Management and Leadership industry executives for AI and Machine Learning Executive Education for MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and invited presentations at Princeton University. After building understanding about why model risk management is most crucial to robust AI, Machine Learning, Deep Learning, and, Neural Networks deployment, it introduces a Knowledge Management Framework for Model Risk Management to advance beyond ‘AI Automation’ to ‘AI Augmentation.’
Keywords: Project Maven, AI, Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks, Model Risk Management, Knowledge Management, AI Augmentation, AI Automation
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