The Performance Pillar of Trusted AI

Trustworthy AI requires accurate, stable, and robust model performance, built on meaningful and high-quality data.

Performance Is Integral to Trusted AI

The performance of a model depends on its accuracy, robustness, stability, and speed. Accuracy alone is too limited to provide a model that’s trustworthy in production or as an integral part of a real-world decision-making process. A mature and responsible AI and machine learning workflow starts with the data, then assesses each of the dimensions of trust explored below.

Trust Dimensions within Performance

The four dimensions of AI Trust that support model performance:

Data Quality

The performance of any machine learning model is directly tied to the data it was trained on. Find out how to frame your approach to assess the trustworthiness of your data.

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Accuracy

It’s important to evaluate the accuracy of your model throughout the development process. Find out how to best leverage metrics and visualizations to evaluate whether you’ve achieved accuracy befitting a production-ready AI system.

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Robustness & Stability

A model in production encounters all sorts of unclean, chaotic data—from typos to anomalous events—which can trigger unintended behavior. Find out how to test whether your model is ready for the real world.

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Speed

The speed of model scoring directly impacts how it can be used in your business process. Find out how the speed of predictions influences their trustworthiness.

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Explore the Concept of Humility in AI

In this ebook, we explore the concept of humility in AI systems and how it can be applied to existing solutions to ensure their trustworthiness, ethicality, and reliability in a fast-changing world.

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