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Track: Emerging Analytics

Engineering Implementation of AI Principles

Wednesday, April 14, 2-2:40pm EDT

Much of the day-to-day use of AI in the past decade has been in the consumer applications on the internet (e.g. simple tasks such as question-answering, product recommendations, etc.) where the consequence of an AI output is relatively minor. With increasing desire to adopt AI in business and mission critical applications, there are major concerns about reliability, trust and ethics underlying these AI applications.

To address these concerns, many government and private organizations (e.g. High-Level Expert Group on AI of the European Commission, Defense Innovation Board of the US Department of Defense, etc.) have defined AI principles to provide guidance for the creation, operation and sustenance of AI systems. These cover wide range of topics such as, appropriate human oversight at all levels, transparency & accountability, privacy & data governance, robustness & safety, etc. As of now, these AI principles are just expressions of desired goals, with no engineering practices to support demonstration of conformance.

The purpose of this talk is to explore the various implications of the AI Principles and develop a measurement framework to help with the realization of trustworthy AI systems. Since Machine Learning is a statistical technique and relies heavily on data, the measurements of AI systems have to reflect that property, similar to Statistical Process Control, commonly used in manufacturing. Metrics have to reflect the variance of the outputs for given inputs, as well as the variance of the outputs for different input data sets in a normalized manner to be meaningful. Such a framework has to include more rigorous engineering practices and analysis of engineering artifacts to demonstrate the fit-for-purpose of AI systems.

Peter Santhanam image

Peter Santhanam

Peter Santhanam

Principal Research Staff Member at IBM Research

Dr. Peter Santhanam is currently a Principal Research Staff Member at IBM Research, working to enable AI in government agencies. Prior to that, Dr. Santhanam worked on several aspects of AI technical strategy and execution in IBM Research. He holds a Ph.D. in Applied Physics from Yale University. In the past twenty five years, he has worked on various research topics in software engineering, particularly in software lifecycle tools, quality management, testing techniques and process improvement. More recently, Dr. Santhanam has been exploring the challenges in the engineering of rapidly evolving AI systems. Dr. Santhanam has more than fifty published peer-reviewed research papers. He is a member of the ACM, member of the AAAI and a senior member of the IEEE. He is also a Member of the IBM Academy of Technology.