AI Quality Metrics: What do they really mean and how do you optimize them for performance?
When applying any AI model as a detector to solve a problem, understanding the four key performance metrics is crucial. These metrics are accuracy, specificity, precision, and recall (some folks like to use F-1, but that's a separate conversation).
Accuracy: How often is the classification correct?
Specificity: How often is a negative classification correct?
Precision: How often is a positive classification correct?
Recall: How often are you able to detect true instances?
Ideally, we would strive for perfect scores in all four metrics. However, in reality, these metrics usually conflict with each other. For example, ensuring that a partially visible human is detected at night might require a lower probability threshold to increase recall (to avoid false negatives), which will also decrease precision (adding false positives).?
So, how does the Camio team tune their AI models to improve performance?
At Camio, we tailor AI models to match the specific application. For example, in security solutions like tailgating detection and other unauthorized entries, we prioritize recall to ensure maximum detection of critical incidents. While this approach can lead to an increase in false positives, we mitigate this with the design of the user experience for efficient triage. By using AI summarization for quick scrubbing of events, Camio makes triaging a one-minute video incident take just a couple seconds.
The result: A system that detects 99.9% of critical incidents, ensuring that nothing slips through the cracks. Operators can quickly validate and respond to alerts, minimizing potential risks.
The alternative: A system that may be highly accurate overall but misses a significant number of critical incidents.
The takeaway: AI is increasingly becoming a part of physical security and operational compliance and understanding how it can be tuned will be critical not only for AI specialists, but for the everyday user as they balance detection versus noise.