Speaker "Agus Sudjianto" Details Back



Toward Trustworthy AI: Explainability and Robustness


All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation. Key factors of managing machine learning model risk is model explainability and robustness. Explainability is critical to evaluate conceptual soundness of models, a fundamental requirement to anticipate potential model failure when models make generalization for situation where they have not been exposed during the training. There are many explainability tools available and my focus in this talk is how to develop fundamentally interpretable self-explanatory models including Deep Learning. Since models in production will be subjected to dynamically changing environments, testing for model robustness is critical, an aspect that has been neglected in AutoML.


Agus Sudjianto is an executive vice president and head of Corporate Model Risk for Wells Fargo, where he is responsible for enterprise model risk management. Prior to his current position, Agus was the modeling and analytics director and chief model risk officer at Lloyds Banking Group in the United Kingdom. Before joining Lloyds, he was a senior credit risk executive and head of Quantitative Risk at Bank of America. Prior to his career in banking, he was a product design manager in the Powertrain Division of Ford Motor Company. Agus holds several U.S. patents in both finance and engineering. He has published numerous technical papers and is a co-author of Design and Modeling for Computer Experiments. His technical expertise and interests include quantitative risk, particularly credit risk modeling, machine learning and computational statistics. He holds masters and doctorate degrees in engineering and management from Wayne State University and the Massachusetts Institute of Technology.