Sunil is the author of 12 books on data management and AI governance, including The IBM Data Governance Unified Process, Selling Information Governance to the Business, Big Data Governance, Data Governance Tools, Data Governance Guide for BCBS 239 and DFAST Compliance, The Chief Data Officer Handbook for Data Governance, and AI Governance.
In the past, Sunil also worked as an auditor at PwC and as a rcs data malaysia management consultant at Booz and Company. Sunil was a member of the Institute of Chartered Accountants of India and has an MBA in Finance from the University of Chicago Booth School of Business. Sunil also holds the Artificial Intelligence Governance Practitioner (AIGP) certification from the International Association of Privacy Professionals. He has also successfully completed the IEEE CertifAIed™ Assessor Training for AI ethics assessments.
By addressing two of the most critical challenges in machine learning – data scarcity and bias – synthetic data generation pushes the boundaries of what ML can achieve across industries by providing a scalable and flexible way to augment real-world data. It also enables the development of more accurate and robust models to push AI to greater heights.
However, synthetic data generation must be used responsibly for the greatest impact. Ethical strategies are paramount to preventing the perpetuation of existing biases encoded in real-world data and ensuring data security, quality, and integrity. Continued research and innovation on computer-generated data will build a future where ML models are more capable, fairer, accurate, and representative of the diversity that makes our world beautiful.