How can you balance AI testing standards and guidelines with time-to-market?

1 Understand the AI testing context

Before you start testing any AI system, you need to understand the context and scope of the project, the requirements and expectations of the stakeholders, and the risks and challenges involved. AI testing context includes factors such as the type, purpose, and complexity of the AI system, the data sources and quality, the algorithms and models used, the performance metrics and criteria, and the ethical and legal implications. By understanding the AI testing context, you can prioritize the most important and relevant aspects of testing and align them with the project goals and timelines.

2 Apply the AI testing principles

AI testing is not just a technical task, but also a creative and analytical one. Consequently, it's important to apply some general principles that guide the testing process and decisions. To begin with, AI testing should start as soon as possible and continue throughout the development cycle using continuous integration and delivery tools and techniques. Additionally, it should cover different levels of the AI system, such as the data, the algorithm, the model, the interface, and the integration with other systems. Furthermore, AI testing should use diverse and representative data sets, test cases, scenarios, and techniques to guarantee that the AI system is robust, fair, and inclusive. Last but not least, AI testing should document and communicate the testing process, results, and issues clearly and effectively to stakeholders and users with visual and interactive tools and reports.

3 Follow the AI testing standards and guidelines

AI testing is a context-specific and dynamic activity, but there are some standards and guidelines that can provide a framework and baseline. These include IEEE P7011, ISO/IEC 23053, AI Ethics Guidelines for Trustworthy AI by the European Commission, Responsible AI Practices by Google, and AI Fairness 360 Toolkit by IBM. These standards and guidelines can help to define and measure the quality attributes of an AI system, such as accuracy, reliability, security, privacy, fairness, explainability, accountability, and social impact. Additionally, they can help to identify and mitigate potential risks and biases like data quality issues, algorithmic errors, model drift, adversarial attacks, ethical dilemmas, and legal challenges.

4 Adapt to the AI testing challenges

AI testing is a dynamic and uncertain activity, requiring you to adapt to changing and emerging challenges. You must ensure the data used to train, validate, and test the AI system is relevant, reliable, diverse, and unbiased. Additionally, you must understand the logic, assumptions, and limitations of the algorithms and models, and use appropriate techniques and tools to test their functionality, performance, and outcomes. AI testing also requires defining and measuring the performance and criteria of the AI system using quantitative and qualitative metrics. Lastly, you should consider the ethical and legal principles that apply to the AI system such as fairness, transparency, accountability, and human dignity.

5 Collaborate with the AI testing team

AI testing is not an individual effort, but a collaborative and integrated one. Therefore, you need to collaborate with the AI testing team, which may include AI developers, testers, analysts, users, and experts. Developers are responsible for designing, developing, and deploying the AI system. Testers are responsible for testing the AI system. Analysts are responsible for analyzing the data, algorithm, and model of the AI system. Users are responsible for using the AI system and providing feedback. Experts are responsible for providing domain knowledge and guidance on the AI system. By working together with the AI testing team, you can benefit from their diverse perspectives and experiences to improve the quality and efficiency of the AI testing process and results.