A key aspect of AI governance is the creation and implementation of evaluation metrics to assess the performance, security, fairness, and effect AI systems have within your organization. Without these metrics, you won’t have a complete understanding of how your AI initiatives are performing in real-world applications. In this article, we’ll delve into some of the essential metrics that can enhance your AI governance framework.
Performance metrics
Performance metrics are important for assessing how well an AI system fulfills its intended functions. It is crucial to employ a variety of performance metrics to ensure the system is not only effective, but also reliable, adaptable, and capable of future value. Some of the more common governance performance metrics include:
Simple accuracy: This metric assesses the frequency in which the AI system makes correct predictions. It is relevant for classification tasks, but may not provide a complete picture of overall performance, especially in cases where class imbalance occurs.
Precision and recall: Precision measures the accuracy of predictions made by the model, while recall measures the ability to find all relevant instances. Both are vital in scenarios such as medical diagnostics, where false positives or false negatives could have significant consequences.
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Tim is a business consultant with a proven track record of helping growth-driven companies in the industrial automation sector, including systems integrators and manufacturers succeed. With his big-picture perspective on how to help closely-held businesses maximize their profitability, Tim is regularly tapped by associations in the industrial automation and automotive sector to interpret the timely topics that keep owners up at night.
