What type of data should be included in AI testing for better reliability?

Including "edge" cases and unseen data in AI testing is critical for achieving better reliability. Edge cases refer to scenarios that occur infrequently but can significantly impact performance or outcomes. Testing with these cases helps uncover potential vulnerabilities and ensures that the AI system can handle a wider range of inputs beyond what it has been trained on.

Unseen data is equally important because it reflects real-world scenarios that the AI may encounter after deployment. If the algorithm is only tested on data it has already seen or on a limited set of previously used datasets, there's a risk that it may not generalize well to new inputs. This can lead to biased results and a lack of robustness in its performance under varied conditions. Including a diverse range of data, particularly edge cases and unseen examples, enhances the AI system's reliability, making it more adept at managing unexpected situations and yielding more accurate predictions in practice.