As businesses start to scale the use of AI as a transformative power to innovate and be more efficient, they have to manage the risks that come from it. Specifically, when dealing with sensitive customer data and in regulated industries, governance is a mandatory aspect of operations. However, as AI becomes more prevalent there are new gaps that need to be addressed in governing the lifecycle of data as well as the models trained on those data. At the same time, governance processes should not impede the iterative nature of data science experiments that help build and operate AI applications. 

 

Join IBM and RBC Bank to hear firsthand why AI Governance is becoming increasingly important in today’s age from governing for control to governing for efficiency and outcomes.

Zain Nasrullah is a Senior Manager in RBC’s Enterprise Model Risk Management (EMRM) department. His team has an enterprise-wide scope that involves validating all material machine learning models to ensure that they are conceptually sound, transparent, and reliable. Zain’s interests involve exploring novel ways of enhancing AI governance in terms of testing (e.g., robustness, uncertainty quantification, fairness, explainability) and process improvement surrounding the model lifecycle (e.g., leading collaborations with internal and external model development teams). He has given several talks and led workshops on machine learning and data analytics in academic and enterprise settings. Prior to joining RBC, Zain worked as a consultant at PwC and project manager at GE Digital. He has also published academic papers in data mining journals and worked on the popular open-source Python Outlier Detection (PyOD) library.