Tea Talk:
Machine Learning, Artificial Intelligence And Risk Management
Machine Learning (ML) is becoming an integral part of everything we do; there’s no getting away from it. We now reach for Google Maps and Waze – which are examples of how far ML has become integrated into our everyday activities – instead of thinking out our routes when we’re travelling. ML systems “understand” our preferences and can “remember” what we have bought, to the extent that they can recommend what to purchase next. While this is a boon to retailers who can angle their marketing in a more targeted manner, the rise of ML and Artificial Intelligence (AI) will see the rise of related risks, in tandem.
At the IERP’s Tea Talk on Machine Learning, Artificial Intelligence and Risk Management, Chairman Ramesh Pillai pointed out that technological changes that were bringing AI and ML into mainstream business was evolving rapidly and driving change. This change, in turn, was creating risk. “As the pace of change accelerates, risk accelerates as well,” he said. Because there was always the possibility of tech tools to be subverted for sinister purposes, there was an urgent need for risk professionals to understand what AI and ML were, i.e., systems that assist businesses in achieving their objectives – and to apply them appropriately.
AI is a concept to create intelligent machines that can simulate human thinking capability and behaviour, whereas ML is an application subset of AI that allows machines to learn from data without being explicitly programmed to do so. Using algorithms that can work with its own intelligence, AI can mimic human intelligence. Some examples already in use include Apple’s SIRI, Google’s AlphaGo, and the AI used in chess playing. It can be classified at three levels: weak AI, general AI and strong AI.
9 The IERP® Monthly Newsletter September - November 2021