How do you check AI systems and interfaces for errors?

1 Define your goals and metrics

Before you start checking your AI system or interface for errors, you need to have a clear idea of what you want to achieve and how you will measure it. What is the purpose of your AI system or interface? What are the expected inputs and outputs? What are the success criteria and the failure modes? How will you evaluate the accuracy, reliability, efficiency, and user satisfaction of your AI system or interface? Having well-defined goals and metrics will help you design your test cases, identify your error sources, and track your progress.

2 Use data validation and verification

One of the most common sources of errors in AI systems and interfaces is the data. Data is the fuel of AI, but it can also be corrupted, incomplete, inconsistent, or biased. To avoid data-related errors, you need to use data validation and verification techniques. Data validation is the process of checking the quality, integrity, and relevance of your data before using it for training or testing your AI system or interface. Data verification is the process of checking the accuracy, completeness, and consistency of your data after using it for training or testing your AI system or interface. Both processes can help you detect and correct data errors, such as missing values, outliers, duplicates, or anomalies.

5 Monitor and update your AI system or interface

The final source of errors in AI systems and interfaces is the environment. Environment is the context and the situation where your AI system or interface operates, but it can also change over time, affecting your AI system or interface performance, relevance, and compatibility. To avoid environment-related errors, you need to monitor and update your AI system or interface regularly. Monitoring is the process of observing and measuring your AI system or interface behavior, performance, and impact in the real world by using various monitoring tools, such as logs, dashboards, alerts, or reports. Updating is the process of modifying and improving your AI system or interface functionality, performance, and impact in response to the changing environment by using various updating methods, such as retraining, reconfiguring, or redeploying. Both processes can help you detect and prevent environment errors, such as performance degradation, irrelevance, or incompatibility.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?