"Trust and confidence building" with AI and ML in automated testing means:

1. _Reliability and accuracy demonstration_: AI/ML models consistently demonstrate reliability and accuracy, building trust.

2. _Transparency and explainability_: AI/ML models provide transparent and explainable results, fostering confidence.

3. _Human-AI collaboration_: SMEs and AI/ML models collaborate, building trust and confidence in joint decision-making.

4. _Continuous improvement and learning_: AI/ML models learn from feedback, improving performance and reinforcing trust.

5. _Regular validation and verification_: Regular validation and verification of AI/ML models maintain trust and confidence.

6. _Domain expertise integration_: Integration of SME domain expertise builds trust in AI/ML models' ability to meet business needs.

7. _Ethical considerations adherence_: AI/ML models adhere to ethical considerations, enhancing trust and confidence.

8. _Accountability and auditability_: AI/ML models provide traceability and auditability, ensuring accountability and trust.

By building trust and confidence in AI/ML-driven testing, organizations can:

- Increase adoption and usage

- Improve testing efficiency and effectiveness

- Enhance decision-making accuracy

- Support digital transformation and innovation

- Foster collaboration between humans and AI/ML