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AI Mitigation · Organizational
Manual QA
Using manual quality assurance tests to verify system accuracy.
📋 Description
Manual Quality Assurance (QA) involves the periodic manual review of AI model outputs to ensure accuracy, relevance, and compliance with expected standards. This mitigation strategy is essential for identifying and addressing issues that automated systems might overlook, such as contextual inaccuracies, ethical concerns, and nuanced errors in model performance. Manual QA also includes pre-deployment reviews, such as code reviews of AI-generated code, to identify potential issues before they affect end users.
Implementation:
- Establish a recurring schedule for manual QA checks pre-deployment and post-deployment.
- Use real-world application data and representative output samples.
- Engage qualified reviewers with domain knowledge and ethical training.
- Include code reviews of AI-generated scripts or applications before they are released to production.
- Document findings and feed them into ongoing system improvements.
📉 How It Reduces Risks
- Catches Contextual and Ethical Errors
- Manual QA identifies subtle or complex issues that automated testing may miss, such as offensive outputs or misinterpretation of context.
- Improves Real-World Performance
- By evaluating outputs in real use cases, manual QA ensures that models behave reliably and accurately in practical settings.
- Enhances Security and Functionality
- Code reviews prevent security vulnerabilities and ensure that generated code functions as intended.
- Supports Compliance
- Helps verify that outputs align with legal, regulatory, and organizational requirements, especially in high-stakes applications.
📎 Suggested Evidence
- QA Review Logs
- Documented records of manual QA sessions, including what was tested and any identified issues.
- Reviewer Guidelines
- Criteria or rubrics used by human reviewers to assess outputs for accuracy, fairness, and compliance.
- Pre-deployment Review Records
- Documentation showing that new models or code were manually reviewed before release.
- Change Logs or Bug Reports
- Logs showing how QA feedback led to model retraining or system adjustments.
- Domain Expert Feedback
- Summaries or reports from qualified reviewers with domain expertise on model performance or risks.
