Machine Learning presents new challenges for governance, risk, compliance, and audit professionals. Some of the top areas of concern are:
For these reasons, and more, we have to be able to understand, inspect, and test the decisions made by ML systems.
Machine Learning Assurance (MLA), is a controls-based process for ML systems that establishes confidence and verifiability through software and human oversight.
Download our whitepaper for a deeper introduction to Machine Learning Assurance and an overview of how Monitaur's software can support your assurance efforts.
In this 10-15 minute read, you’ll gain insights into:
Machine Learning (ML) is a form of artificial intelligence whereby computer systems recognize patterns and make predictions or decisions without explicit programming.
Machine learning models are the mechanisms needed for a machine to recognize patterns and learn how to make decisions. A machine learning model is comprised of training data, algorithms, and other important information.
Machine learning assurance (MLA)
MLA is a controls-based process for ML systems that establishes confidence and verifiability through software and human oversight.
AI regulations are laws that define how organizations use and report on AI systems, especially when it comes to consumer privacy, ethical practices, and transparency.
Cross-Industry Standard Process for Data Mining is a framework for how ML is conducted by practitioners. CRISP-DM was specifically tailored to enhance machine learning assurance in 2018.
The high-level steps of CRISP-DM methodology include:
Download our whitepaper to learn more about our expanded methodology that includes the top 10 controls for machine learning.