The Quality Engineering (QE) evolution for the Artificial Intelligence (AI) revolution

Comprehensive Test Planning

A well-defined test plan should continue to outline the objectives, scope, resources, and timelines for testing AI systems. Test plan scope should now include risk assessments that identify potential ethical concerns, biases, and compliance with regulations.

The planning process should also involve stakeholders from various organisational functions including development, operations, legal, and ethics teams. By collaborating with diverse teams, organizations can ensure that all relevant perspectives and requirements are considered, leading to a more holistic approach to testing.

Diverse Test Cases

Testing AI systems requires a diverse set of test cases that cover various scenarios, including edge cases and exception paths. This diversity helps ensure that the AI behaves as expected across different contexts and populations, reducing the risk of incorrect, hallucinations or biased outcomes.

By including diverse test cases, organizations can identify potential biases and unintended consequences that may arise from the AI's decision-making processes. This approach helps ensure that the AI behaves as expected across different contexts and populations, ultimately reducing the risk of biased outcomes.

Data Quality and Integrity

The quality of data used to train AI models is critical. Test data remains the Achilles heel of many organisations still, now the importance of a test data strategy is amplified. The test strategy should include validation of data sources, data cleansing processes, and checks for representativeness. Ensuring data fidelity helps mitigate biases and enhances the reliability of AI outputs.

Continuous process and data quality monitoring will validate the accuracy, completeness, and relevance of the data used for training and testing across all non-production environments. 

Explainability and Transparency

AI systems should be transparent in their decision-making processes. Testing should evaluate the explainability of AI models, ensuring that stakeholders can understand how decisions are made. This is crucial for building trust and accountability.

Explainability is crucial for building trust among users and stakeholders. Organizations should define mechanisms that provide clear explanations of AI decisions, allowing users to comprehend the rationale behind outcomes. This transparency is essential for accountability and ethical AI deployment.

Performance and Scalability

AI systems must perform efficiently under varying loads. Performance testing should assess how well the AI system scales with increased data and user demands, ensuring that it remains responsive and effective.

Scalability testing should also consider the system's resilience to handle diverse data inputs and adapt to changing environments. By ensuring that AI systems can scale effectively, organizations can avoid performance bottlenecks and maintain a high level of service quality.

Security

The mitigation of threats and vulnerability risk for AI systems and underlying components must be the focus within the strategy and underlying test plans. Test plans need to provide coverage for data, models and infrastructure components to ensure the whole, integrated system functions reliably and safely.

Frequent code reviews, penetration testing and security audits and are essential static and dynamic testing activities to detect and mitigate potential threats throughout the delivery lifecycle.

Compliance and Ethical Standards

A responsible AI test strategy must align with legal and ethical standards. This includes adherence to data protection regulations, industry standards, and ethical guidelines. Organizations must remain informed about relevant laws and regulations and adapt the testing process where necessary to remain compliant.

Regular audits and compliance checks should be integrated into the testing process through the test governance framework or entry/exit criteria.

Continuous Monitoring and Feedback Loops

AI systems should be continuously monitored post-deployment to identify any unintended consequences or performance issues. Adapting DevOps and Service Management capabilities to establish feedback loops allows for iterative improvements and ensures that the AI system remains continually aligned with ethical and regulatory standards.

Monitoring should include tracking key performance indicators (KPIs) related to the AI system's effectiveness, fairness, and compliance. By analysing this data, Service Management can make informed decisions about necessary adjustments, patches and enhancements to the AI system and underlying infrastructure platforms.

Stakeholder Involvement

Engaging stakeholders, including users, domain experts, and ethicists, throughout the testing process is vital. Their insights can help identify potential risks and ethical concerns that may not be apparent to developers or test engineers alone.

Organizations should create channels for stakeholder feedback throughout the testing process. This collaborative approach fosters a sense of ownership and accountability, ultimately leading to more responsible AI systems.

The “earlier a defect is detected, the more economical it is to fix” mantra still prevails.

Summary

A comprehensive software test strategy is essential for the responsible development and deployment of AI systems. By focusing on diverse test cases, data integrity, explainability, compliance, and continuous monitoring, organizations can build AI solutions that are not only effective but also ethical and trustworthy.

As AI continues to revolutionise our world, prioritizing responsible, quality-driven practices will be key to fostering innovation while safeguarding societal values. The time to evolve Quality Engineering and Testing is now.

Next Steps

To receive your copy of our AI Test Strategy ‘Getting Started Guide’ or to find about more information about how to future-proof your Quality Engineering, Quality Assurance or Testing organisation for responsible AI please do not hesitate to contact us.