How to Achieve Ethical Quality Assurance (QA) for Your Software Using Artificial Intelligence (AI)
AI can facilitate more efficient software testing processes, but it can also introduce risks to your business if you’re not careful. These are the measured steps you should take to maintain trust in your testing outcomes and the software’s performance and objectivity.
Why You Should Read This Post
As the use of artificial intelligence (AI) for software testing and quality assurance (QA) becomes increasingly prevalent, there are ethical considerations that must be addressed to ensure fairness, transparency, and accountability.
The Case for AI
AI has the potential to revolutionize software QA by enhancing the efficiency and accuracy of testing processes. Traditional manual testing is time-consuming and prone to errors, whereas AI-powered testing tools can analyse code more comprehensively and at a larger scale. Despite these advantages, the implementation of AI in QA requires thoughtful deliberation on ethical aspects.
Caution: Pitfalls Ahead
One significant concern is bias in AI algorithms. The training data used to develop AI systems must be diverse and representative of the target population to avoid biased outcomes. Biased AI can lead to unfair or discriminatory decisions, particularly in sensitive areas such as hiring and promotions. It is essential to rigorously test AI models to identify and mitigate biases, ensuring that the software solutions are fair and equitable. This article, written by several QA and AI experts, is an excellent guide:
How do you test AI models in an agile way?
Data privacy is another critical issue. AI systems often process large volumes of personal and sensitive information, which can pose privacy risks. Adhering to data privacy regulations, such as the General Data Protection Regulation (GDPR), is crucial. Techniques like data anonymization, synthetic data usage, and data masking can help protect user privacy during testing. Moreover, maintaining transparency in AI decision-making processes builds trust among users and stakeholders. Clear documentation and reporting of testing procedures and outcomes are vital for accountability.
Human oversight remains indispensable in AI-powered testing. While AI can handle specific tasks efficiently, human creativity, intuition, and context-awareness are irreplaceable. Continuous monitoring and review of AI decisions by human testers ensure that any discrepancies or errors are promptly identified and addressed. This human-AI collaboration fosters a more robust and reliable QA process.
How to Avoid These Risks When Using AI to Test Software
To implement ethical QA effectively, your organization should adopt inclusive testing practices that involve diverse teams to evaluate software from multiple perspectives. Ongoing training on ethical QA and understanding the latest developments in the field helps maintain high standards and promotes a culture of responsibility.
Rather than replacing human testers, the key is determining how to best augment human expertise with AI. It would be a mistake to assume AI can entirely replace human testers. Human insight remains inimitable when it comes to software testing. Critical thinking enables nuanced problem solving and debugging that goes far beyond AI’s current abilities.
Also, one of the biggest mistakes organizations make with AI testing is failing to dedicate enough time and resources to gathering sufficiently robust and varied training datasets.
Without comprehensive and accurate training data that directly represents real-world usage of the application, AI testing tools will inevitably provide skewed or limited results. Any biases or lack of diversity in training data then gets amplified in the AI system’s performance. Once initially deployed, AI testing models cannot be left to operate in a fixed state indefinitely. As software applications evolve rapidly, model accuracy and reliability will decay over time without continuous governance. Rigorously monitoring performance, coupled with regular model updating and retraining, is imperative. This includes:
A/B testing new models against existing versions. In A/B testing, you compare the performance of two models against a specific version to measure which one is most successful based on your key metrics.
triggering automatic retraining when certain thresholds are crossed. For example, if less than 1% of requests return an error, if at least 95% of requests have a response time below 200ms, or if a specific endpoint always responds within 300ms, then retraining is required.
retiring models that become obsolete or counterproductive.
continuously assessing performance.
tuning models. This is the process of taking a pre-trained model and adjusting it to better fit your data and then improving the model’s performance by modifying its configuration, code, or architecture without changing its functionality or behaviour.
enhancing training data to strengthen reliability over time. For example, mock data (null values, out of range values and special characters) can easily identify if a program can handle exceptions correctly.
Looking Ahead
The role of ethical QA will become increasingly significant as technology advances.
For example, with Generative AI (GenAI) now being used to automate test case creation, synthesize data, enhance bug detection, streamline both regression testing and code generation, and speed up releases, it’s essential to address challenges related to quality, bias, and transparency.
There are also ethical considerations with agentic AI considering this type of system that is designed to operate autonomously, making decisions and taking actions based on its programming, goals, and the data it receives. In software, agentic AI involves AI-driven test automation, which in turn use machine learning and agentic capabilities to autonomously generate, execute, and adapt tests.
Therefore, both GenAI and agentic AI can affect ethical issues and risks surrounding data privacy, security, policies and workforces (misinformation, copyright infringements and legal exposure, data privacy violations, sensitive information disclosure).
By prioritizing fairness, transparency, and accountability, ethical QA can enhance the quality of software products and help ensure that development processes respect and uphold ethical standards. This commitment to ethical principles in AI-powered testing paves the way for a more inclusive and responsible future in software development.
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