"Audit trails" with AI and ML in automated testing means:

1. _Detailed logging_: AI/ML models log all activities, including testing, training, and updates.

2. _Timestamps and user tracking_: Audit trails include timestamps and user tracking, identifying who made changes and when.

3. _Change description and justification_: Audit trails include descriptions and justifications for changes, providing context.

4. _Data versioning and tracking_: Audit trails track changes to data, including versioning and updates.

5. _Model performance and accuracy tracking_: Audit trails monitor model performance and accuracy, identifying areas for improvement.

6. _Compliance and regulatory adherence_: Audit trails ensure compliance with regulations, such as data privacy and security.

7. _Error and exception tracking_: Audit trails track errors and exceptions, facilitating debugging and improvement.

8. _Transparent decision-making processes_: Audit trails provide insights into AI/ML decision-making processes, ensuring transparency.

By maintaining audit trails, AI and ML in automated testing can:

- Ensure accountability and transparency

- Support compliance and regulatory adherence

- Facilitate error analysis and debugging

- Enable continuous improvement and learning

- Provide insights into AI/ML decision-making processes