COSO Releases GenAI Risk Management Guide

- This guidance builds upon COSO's existing Enterprise Risk Management (ERM) framework, which is a widely accepted model for risk management, and adapts its principles to the unique challenges of generative AI. - A key focus of the guide is on data governance, outlining the need for robust processes to manage the data used to train and operate GenAI models, especially when dealing with sensitive information like student data. - For an AI-powered reading tutor, this framework would emphasize the need to mitigate algorithmic bias to ensure the tool is fair and effective for children from diverse backgrounds and with different learning styles. - The guide's principles align with the need for privacy-preserving machine learning techniques, such as federated learning and differential privacy, to protect children's data. - In the context of speech recognition for young learners, the framework underscores the importance of model robustness to account for the variability in children's speech patterns, accents, and developmental stages. - The guidance encourages the use of explainable AI (XAI) techniques to make the AI's decision-making process transparent and understandable to educators and developers, a crucial aspect for adaptive learning systems. - For reinforcement learning models that adapt educational content, the framework would necessitate implementing safety constraints to prevent the recommendation of inappropriate or unhelpful material. - The guide was developed in collaboration with Deloitte, which also offers a "Trustworthy AI" framework that complements COSO's principles with a focus on six key dimensions: fairness and impartiality, transparency and explainability, responsibility and accountability, robustness and reliability, privacy, and safety and security.