"Type I error control" with AI and ML in automated testing means:

1. _Alpha level adjustment_: AI and ML dynamically adjust the alpha level (typically 0.05) to balance false positives and false negatives.

2. _Threshold optimization_: AI and ML optimize thresholds for anomaly detection to minimize Type I errors.

3. _Test statistic modification_: AI and ML modify test statistics to reduce the likelihood of Type I errors.

4. _Multiple testing correction_: AI and ML apply corrections (Bonferroni, Holm-Bonferroni, etc.) to account for multiple tests.

5. _False discovery rate (FDR) control_: AI and ML control the FDR to limit the proportion of false positives.

6. _Per-family error rate (PFER) control_: AI and ML control the PFER to limit the error rate across multiple tests.

7. _Machine learning model selection_: AI and ML select models that minimize Type I errors, such as logistic regression or decision trees.

8. _Hyperparameter tuning_: AI and ML tune model hyperparameters to optimize Type I error control.

By controlling Type I errors, AI and ML in automated testing can:

- Reduce false alarms and unnecessary interventions

- Improve test accuracy and reliability

- Increase trust in automated testing results

- Optimize process performance and quality