Testing AI applications and ML models: Revealing proven quality assurance strategies and techniques

In an era where artificial intelligence (AI) and machine learning (ML) are revolutionizing industries like healthcare, finance, and beyond, the stakes for quality assurance (QA) have never been higher. Unlike traditional software with predictable logic, AI/ML systems are dynamic, data-driven, and ever-evolving, presenting unique challenges that demand cutting-edge testing strategies. 

Businesses that prioritize robust quality assurance strategies for testing AI applications and ML models will not only ensure reliability, accuracy, and fairness but also gain a competitive edge, fostering trust and driving innovation. This blog explores these challenges and unveils strategic QA approaches to deliver high-performing and ethical AI solutions.

How are quality assurance strategies changing AI/ML application testing dynamics over traditional manual testing approaches?

Using Artificial Intelligence and Machine Learning in testing is reforming the process of quality assurance. Standard rules are often not enough for AI/ML since these systems act in an unpredictable way due to probabilistic methods. AI/ML models such as recommendation engines and chatbots, unlike traditional software, do not always give the same answers for the same inputs. For this reason, software engineers use adjustable test cases and math calculations to confirm the application’s acceptable performance.

Test engineers face issues when working with large datasets and complicated models in AI/ML systems. Data validation, bias detection, and supervising models all now rely on automated tools. Great Expectations and Fairlearn ensure data accuracy and fairness across large amounts of data, while adversarial testing and detecting drift help address data drifts and weak spots in models.

It is important to add continuous testing to CI/CD because AI models often improve through retraining. Before new versions are released, automated systems see to it that the required performance standards are met. AI QA experts should acquire new knowledge, such as machine learning basics, how to interpret data (with tools such as SHAP and LIME), and how to deal with fairness and compliance in AI systems. Both ethical and interpretability audits are important practices nowadays to ensure that AI/ML systems are easy to understand, fair, and adhere to laws.

Understanding the unique challenges of testing AI applications and ML model testing

• Non-deterministic behavior

In traditional software, the actions are clear and set, but with AI/ML, things are less certain because of the dependency on probability and data. For this reason, a recommendation engine may recommend certain products to a user twice, even though the conditions are exactly the same, because there have been minor changes in the model or data inputs. This unexpected behavior makes testing harder, since regular types of test cases do not address the issue.

• Data dependency and bias

The usefulness of AI/ML systems depends on the quality of information they are trained with. Inaccurate results or discrimination can occur when the data provided is inadequate or misleading. When a facial recognition system is not exposed to varied images, it might incorrectly identify people from underrepresented groups. Evaluation must ensure that data is high-quality, diverse, fair, and that the model works well when presented with new data.

• Black-box nature

Many AI/ML models, especially deep learning systems, operate as "black boxes," with opaque internal decision-making processes. This lack of interpretability makes it difficult to pinpoint why a model fails or produces unexpected results, complicating debugging and validation efforts.

• Evolving models

To keep getting better, AI/ML models are updated or retrained on an ongoing basis. When update changes are released, they can create new issues or remove previous improvements, so you must continue testing to keep things stable. While traditional software updates often add just a little, updates to a machine learning (ML) model may cause significant changes, so the process must be more robust.

• Ethical and regulatory compliance

AI/ML applications often operate in sensitive domains like healthcare, finance, or hiring, where ethical concerns and regulatory requirements (e.g., GDPR, CCPA, or FDA guidelines) are critical. Testing must verify that models adhere to fairness, privacy, and accountability standards, avoiding biases or unintended consequences.
Performance at scale

The journey of testing AI applications and ML model testing often includes processing massive datasets or serving millions of users in real-time. Performance testing must evaluate scalability, latency, and resource efficiency under varying loads, ensuring the system remains reliable in production environments.

Quality assurance strategies and techniques for AI/ML Applications

To address these challenges, organizations must adopt tailored QA strategies that blend traditional software testing principles with AI-specific methodologies. Below are proven approaches to ensure high-quality AI/ML applications.

1. Data-centric testing

Data is the backbone of AI/ML systems. Rigorous testing ensures quality via validation for completeness, accuracy, and consistency, bias detection using fairness metrics, and augmentation with synthetic data. This ensures robust and unbiased models, like healthcare AI, predicting outcomes across diverse patient groups without skewed results. Implement the following:

• Data validation: Verify data quality by checking for completeness, accuracy, and consistency. Use statistical tests to identify outliers, missing values, or anomalies. For example, a healthcare AI model predicting patient outcomes should be tested with diverse patient data to avoid skewed results.
• Bias detection: Employ fairness metrics (e.g., demographic parity, equal opportunity) to identify and mitigate biases in training datasets. Tools like Fairlearn or AI Fairness 360 can quantify disparities across protected groups.
• Data augmentation: Test model robustness by augmenting datasets with synthetic or adversarial examples to simulate edge cases or rare scenarios.

2. Model interpretability and explainability

Address AI’s black-box nature by using interpretable models like decision trees or tools like SHAP and LIME for complex models. Conduct adversarial testing and sensitivity analysis to ensure transparency and robustness, which are critical for trust in high-stakes applications like medical diagnostics or financial decision-making.

• Use interpretable models: For tasks requiring high transparency, opt for simpler models (e.g., decision trees). For complex models like neural networks, integrate explainability tools like SHAP (SHapley Additive ExPlanations) or LIME (Local Interpretable Model-agnostic Explanations) to understand feature importance and decision pathways.
• Adversarial testing: Generate adversarial inputs to test model robustness and uncover vulnerabilities. For instance, slightly perturbing an image input to a computer vision model can reveal if it misclassifies objects.
• Sensitivity analysis: Test how sensitive the model is to changes in input features, ensuring stability and reliability.

3. Continuous testing for evolving models

Automated retraining pipelines with CI/CD, regression testing to prevent performance drops, and drift detection using tools like Evidently, AI ensures models adapt to changing data. Over time, this maintains accuracy in applications like chatbots or fraud detection. AI/ML models require ongoing testing to maintain performance:

• Automated retraining pipelines: Implement CI/CD pipelines with automated testing to validate model updates. Use version control for models and datasets to track changes and rollback if necessary.
• Regression testing: After retraining, compare new model outputs against baseline performance metrics to detect regressions. For example, a chatbot’s accuracy in intent recognition should remain consistent post-update.
• Drift detection: Monitor data drift (changes in input data distribution) and concept drift (changes in the relationship between inputs and outputs) using tools like Evidently AI or Alibi Detect.

4. Ethical and compliance testing

Ensure AI aligns with ethical and regulatory standards through fairness audits, privacy testing with differential privacy, and compliance with laws like GDPR or FDA regulations. This prevents bias, protects user data, and ensures legal alignment in sensitive domains like hiring or healthcare. Here’s how:

• Fairness audits: Conduct regular audits to assess model fairness across demographics. For instance, a hiring algorithm should be tested to ensure it doesn’t favor one gender or ethnicity.
• Privacy testing: Validate compliance with data protection laws by testing for data leakage or unauthorized access. Techniques like differential privacy can be integrated to protect sensitive information.
• Regulatory alignment: Map testing processes to domain-specific regulations, such as FDA requirements for medical AI or GDPR for user data handling.

5. Performance and scalability testing

Test AI under real-world conditions with load testing for high traffic, stress testing to find breaking points, and edge deployment testing for resource-constrained devices. Tools like Locust ensure systems like recommendation engines perform reliably while maintaining efficiency and resilience in production environments.

• Load testing: Simulate high user traffic to assess latency, throughput, and resource consumption. Tools like Locust or JMeter can help.
• Stress testing: Push the system beyond normal operational limits to identify breaking points, ensuring resilience in edge cases.
• Edge deployment testing: For AI models deployed on edge devices (e.g., IoT sensors), test performance under constrained resources like limited memory or processing power.

6. End-to-End testing with real-world scenarios

Validate AI with real-world simulations via scenario-based testing, A/B testing for model comparison, and user acceptance testing. This ensures systems like recommendation engines or chatbots handle diverse inputs, meet business needs, and deliver seamless user experiences in practical, real-world applications. Here are simulated real-world use cases to validate system performance:

• Scenario-Based testing: Create test cases mirroring actual user interactions. Test how a recommendation system handles diverse user profiles or incomplete data.
• A/B testing: Deploy multiple model versions in production to compare performance metrics like user engagement or conversion rates.
• User acceptance testing (UAT): Involve end-users to validate that the AI/ML application meets business requirements and delivers a seamless experience.

Building a QA-centric culture for AI and ML applications

Making an effective quality assurance (QA) framework for AI/ML requires the organization to value QA traits and practices in its culture. Their approach is based on teamwork, being responsible, and regularly improving the AI/ML systems to guarantee reliability, ethics, and results that support the business. Below, we elaborate on the key pillars of building this culture: cross-functional collaboration, documentation and traceability, and continuous learning.

1. Cross-functional collaboration

AI/ML development and testing involve diverse skill sets, from data science and engineering to domain-specific expertise. A siloed approach—where data scientists build models, QA engineers test them, and domain experts provide input in isolation—can lead to misaligned priorities, overlooked edge cases, and suboptimal outcomes. Cross-functional collaboration bridges these gaps, fostering a unified approach to quality assurance.

• Involving diverse stakeholders: Engage data scientists, QA engineers, software developers, domain experts (e.g., medical professionals for healthcare AI), and business leaders early in the development lifecycle. For example, in a financial AI application for credit scoring, domain experts can define acceptable risk thresholds, while data scientists ensure the model’s predictive accuracy, and QA engineers validate its performance across diverse scenarios.
• Aligning with business goals: Cooperating with others allows the process to match the business’s main objectives, including improving what customers experience or following necessary rules. Reviewing requirements with other teams consistently will ensure that a chatbot’s messages are not culturally offensive and that a predictive maintenance approach helps avoid many stops in manufacturing.
• Iterative feedback loops: QA teams share their tests with data scientists, who then work to fix and tune the models, while experts analyze how the models work with real-life data. So, if a healthcare AI doesn’t detect a rare disease properly, domain experts can help by giving it access to more information about such diseases.
• Agile methodologies: Adopt techniques such as Scrum or Kanban through Agile Methodologies to encourage everyone to collaborate. Frequent stand-ups or collective retrospectives keep the team unified, let them deal with issues right away, and allow them to improve their testing processes.

When people collaborate in AI and ML, the chances of missing key points decrease, the reliability of the model increases, and the business receives value from the technology.

2. Documentation and traceability

AI/ML systems are complex, with multiple components—datasets, model architectures, training pipelines, and test cases—evolving over time. Comprehensive documentation and traceability are essential to maintain transparency, ensure reproducibility, and meet regulatory requirements.

• Dataset documentation: Record details about training, validation, and test datasets, including sources, preprocessing steps, and demographic distributions. For example, in a hiring AI, document whether the dataset includes diverse candidate profiles to prevent bias. Tools like Data Version Control (DVC) can track dataset versions.
• Model versioning: Maintain a clear history of model versions, including hyperparameters, training configurations, and performance metrics. This enables teams to trace performance changes or regressions after retraining. Platforms like MLflow or Weights & Biases facilitate model versioning and experiment tracking.
• Test case traceability: Document test cases, including inputs, expected outputs, and actual results, to ensure reproducibility. For instance, in a computer vision model for autonomous driving, log test scenarios like low-light conditions or adverse weather to verify robustness. Link test cases to specific requirements or regulatory standards for auditability.
• Compliance and audit readiness: In regulated industries like healthcare or finance, traceability ensures compliance with standards like GDPR, CCPA, or FDA guidelines. For example, document how a medical AI adheres to HIPAA by anonymizing patient data during testing.
• Knowledge sharing: Centralized documentation fosters knowledge transfer across teams, reducing dependency on individual contributors. Use wikis or platforms like Confluence to store and share documentation, ensuring accessibility for new team members or auditors.

By prioritizing documentation and traceability, organizations can debug issues faster, reproduce results reliably, and demonstrate compliance, building trust in their AI/ML systems.

3. Continuous learning

AI/ML technologies evolve rapidly, with new algorithms, frameworks, and testing methodologies emerging regularly. To stay competitive, teams must adopt a culture of continuous learning, equipping themselves with the skills to address AI-specific challenges.

• Training on AI-specific testing: Provide regular training on AI/ML testing techniques, such as adversarial testing, bias detection, or drift monitoring. For example, QA engineers should learn to use tools like SHAP for model interpretability or Evidently AI for detecting data drift. Workshops or certifications from platforms like Coursera or Udemy can upskill teams.
• Staying updated on industry trends: Encourage teams to follow advancements in AI testing through conferences (e.g., NeurIPS, ICML), research papers, or industry blogs. For instance, staying informed about new fairness metrics can help teams proactively address bias in predictive policing models.
• Hands-on experimentation: Foster a culture of experimentation by providing access to sandboxes or test environments where teams can explore new tools or techniques. For example, experimenting with adversarial attack libraries like Foolbox can help QA engineers identify vulnerabilities in image recognition models.
• Cross-disciplinary learning: Encourage data scientists to learn QA principles, such as test case design, while QA engineers gain basic knowledge of ML concepts like overfitting or feature engineering. This mutual understanding enhances collaboration and improves testing outcomes.
• Learning from failures: Treat testing failures as learning opportunities. Conduct post-mortems to analyze why a model failed (e.g., a recommendation system suggesting irrelevant products) and share insights across teams to prevent recurrence.

Having QA become a core cultural value results in real benefits. Let’s say a retail company installs an AI-powered recommendation system. Joining forces between data scientists, QA engineers, and marketing experts will help them prevent recommending useless products to customers. A complete set of documents makes sure the system is in compliance with laws like GDPR, and learning at all times allows the team to embrace methods such as detecting real-time drift to keep pace with shifting customer interests.

Conclusion

Testing AI applications and ML model testing is a complex but critical endeavor. From data validation to continuous testing and regulatory compliance, a robust quality assurance strategy ensures AI/ML applications meet user expectations and industry standards. At Kellton, we help businesses to address the critical QA testing lifecycle challenges through quality assurance strategies and techniques to deliver high-performing AI systems.