A.I. Tools

Organizational Processes for Machine Learning Risk Management | by Parul Pandey | Sep, 2023

In our ongoing series on Machine Learning Risk Management, we’ve embarked on a journey to unravel the critical elements that ensure the trustworthiness of Machine Learning (ML) systems. In our first installment, we delved into “Cultural Competencies for Machine Learning Risk Management,” exploring the human dimensions required to navigate this intricate domain. The insights presented therein lay the foundation for our current exploration, and therefore, I highly recommend that you go through the part before continuing with this article.

In this second article, we pivot our focus to another vital element in the context of ML systems: Organizational Processes. While technical intricacies often overshadow these processes, they hold the key to guaranteeing the safety and performance of machine learning models. Just as we recognized the significance of cultural competencies, we now acknowledge that organizational processes are the foundational cornerstone upon which the reliability of ML systems is constructed.

This article discusses the pivotal role of organizational processes in the realm of Machine Learning Risk Management (MRM). Throughout the article, we emphasize the criticality of practitioners meticulously considering, documenting, and proactively addressing any known or foreseeable failure modes within their ML systems.

While it is crucial to identify and address possible problems in ML systems, turning this idea into action takes time and effort. However, in recent years, there has been a significant increase in resources that can help ML system designers predict issues more systematically. By carefully sorting out potential problems, making ML systems stronger and safer in real-world situations becomes easier. In this context, the following strategies can…


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