Have you started using AI in your application development? Yes? Then, you must have realized that testing these AI applications is completely different compared to your traditional software testing practices. This is because when you are trying to test AI capabilities, you are actually aiming to verify the functioning of robust and complex machine learning models.
Struggling with AI testing? Don’t worry! We are here for you. Our article will help you improve your idea about the concept of strategic AI testing. During this process, we’ll also explain how organizations can build and implement intelligent test frameworks to ensure the integrity, accuracy, and robustness of AI systems.
What Is AI Testing
Before we dive into all the complex details we must take a step back and begin our understanding with the basics of AI testing. You must have seen that artificial intelligence has massively changed in recent years due to major innovations in deep learning, growing data availability, and improved computational power.
So, the traditional rule-based expert systems have also been obsoleted by machine learning models that can adapt and learn complex patterns from data. Now, you can use AI to make recommendations for online shopping, drive cars autonomously, detect disease from medical images, and also process insurance claims, to name a few.
However, you must be aware that the introduction of complex machine learning algorithms also brings in certain uncertainties. To shed more light over this segment, we have listed some of the major inclusions in this segment:
- The predictive power of artificial intelligence will be restricted by the quality of the data that you are using to train it.
- You will start to see biases in training data or moral design, which can cause unethical outcomes or unwanted discrimination during the implementation process.
- In certain cases, deep neural networks will act as black boxes which will make it very difficult to understand. In certain cases, decisions might be made by these models.
- It is inevitable that, in certain cases, the AI systems will degrade over time or fail unexpectedly when subjected to multiple changing environments.
How AI Testing Differs from Traditional Software Testing
It is very important to have a clear idea about the major differences between traditional software testing and AI testing. Conventionally, software testing focuses on verifying a system’s output against specified requirements and also a defined set of inputs. To implement this, the QA teams will create test cases specifying the requirements, inputs, and expected outputs.
But in an AI environment, especially the machine learning models, you do not have to follow these deterministic rules derived from specifications. Instead, you will learn from statistical relationships that are implemented from the data. Let us now drive our attention towards some of the major strategies that you must implement while using AI testing:
- AI models will generate a probability distribution instead of a single deterministic output. So, the teams must consider the confidence and likelihood of predictions rather than a simple pass-or-fail outcome.
- Machine learning models are usually trained to optimize objective failures like accuracy or F1 score. However, in a real-world scenario, this will shift over time or will be context-specific. Therefore, you must have adequate coverage of scenarios and environmental changes to properly evaluate the parameters.
- It is very important to test the data that you will be using to train the AI models. In most cases, biased or low-quality data will cause significant issues with the dependency and accuracy of the test reports.
- AI models will experience model drift or concept drift when the real-world data distribution changes. Therefore, you must implement continued performance checks to maintain the reliability of this data over time.
- The process of understanding model decisions and ensuring fairness are certain practices that were not previously present while working with traditional software testing practices.
Foundations of Strategic AI Testing
Let us now divert our attention towards some of the fundamental parameters that you must keep in mind to implement an efficient foundation of strategic AI testing framework:
- The first thing is to ensure a shift lift approach to data quality. This means that you must implement the testing processes at earlier phases of the development cycle. This approach not only helps find errors at the initial stages but also ensures timely delivery of the complete software.
- Modern AI applications are constantly used in dynamic and real-world environments. So, your testing scenarios must include edge cases, extreme values, and diverse behaviors. You should also consider using synthetic data generation in certain scenarios where real-world data might not be available.
- You must remember that only relying on a single metric like accuracy will not be sufficient when working with artificial intelligence models. Therefore, you must include multiple metrics like precision recall, F1 score, and domain-specific KPIs to capture the broader performance of the entire application.
- You must remember that the artificial intelligence models will fail unpredictably under adversarial or unknown inputs. Therefore, you must test the model for robustness, noise tolerance, or out-of-distribution data scenarios. It is also important to understand how the model reacts when it comes to unaccounted user interactions.
- As we already mentioned earlier, your artificial intelligence model will embed societal biases that might be somewhere within the training data. Therefore, it is very important to use strategic AI testing to include bias detection, fairness evaluation, and continuous monitoring of demographic-specific performance.
- The final step in this process is to integrate the AI systems with continuous integration and continuous deployment pipelines, just like any contemporary software. This should also include frequent automated tests and monitoring for performance drift. Moreover, if you are planning to retrain the model regularly, you must verify the new training data and validate it before deployment.
Designing Intelligent AI Test Frameworks
An intelligent AI testing framework often symbolizes a combination of processes, tools, templates, and guidelines which will help you implement consistent automated and systematic AI testing pipelines. Let us take a quick look at some of the major factors that you must consider in this regard:
- You must begin your understanding with the pipeline integration process. This step will consist of multiple individual subtasks like data preprocessing, model training, validation and testing, and also deployment. The primary task of this step is to ensure that the model is ready and successfully deployed.
- The next step is breaking down the testing framework into modular components, including data validation module, model evaluation module, performance monitoring module, explainability and fairness module, and the reporting and dashboard module.
- The next step is including automation to execute large portions of AI testing which will include huge volumes of data and frequent retraining cycles.
- Consider integrating your AI testing cycles with DevOps toolchain. This is because the integration with version control systems, continuous integration servers, and artifact repositories will help streamline the entire application development and testing cycle.
- The final step in this process is to focus on role definitions and team member collaborations. This is because AI testing will include various domains like data engineering, ML research, software engineering, DevOps, and quality assurance. So you must have clarity regarding all the steps and all the team members must be in sync about the current updates of the training and testing cycle.
Tools and Technologies Enabling Intelligent AI Testing
To build an intelligent AI testing framework, you can often combine open-source libraries, cloud services, and commercial tools that can help you at different stages of the testing process. To help you better understand this step, we have mentioned some of the most popular tools that are currently available in this segment:
- For data validation and quality, you can use open-source libraries like Great Expectations, which can help you define, execute, and monitor data frameworks. You can also use Deequ, a framework from AWS which uses Spark for data quality checks.
- To implement experiment management and model tracking, you can use tools like MLFlow. You can also consider including Weights and Biases to implement hyperparameter management and model performance visualization.
- If you want to perform AI testing on real devices, you can consider using cloud platforms like LambdaTest. LambdaTest is an AI-powered test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000 real devices, browsers, and OS combinations. Some of the other features include access to legacy browsers and devices, comprehensive test reporting, and native bug tracking.
- If you want to implement model evaluation and thorough testing, you can use Pytest along with custom frameworks that can help verify the functioning of machine learning test scripts. TensorFlow Extended will also offer features like data analysis components, model verification components, and model evaluation capabilities.
- LIME or Local Interpretable Model Agonistic Explanations is a tool that will assist you in explaining individual predictions, while SHAP or SHapley Additive exPlanations, will help you estimate feature contributions for specific model inputs.
- Tools like Foolbox and CleverHans will be highly suited for performing adversarial testing as they come with native libraries of adversarial attacks and differences for benchmarking security on your chosen machine-learning model.
- Finally, you can rely on tools like Evidently and WildLaps to implement real-time model monitoring, drift detection, and performance dashboards.
We would like to reiterate on our point that the integration of these tools, along with well-defined processes and custom code, will help you to build comprehensive AI testing pipelines which can handle the nature of ML-based application development cycles.
The Bottom Line
Based on all the factors that we have put forward in this article, we can safely say that strategic AI testing is very important for organizations who will be adopting machine learning and other advanced AI technologies. To ensure reliability, transparency, and fairness in AI practices, you must closely collaborate with the target stakeholders for designing intelligent test frameworks which can seamlessly verify the functioning of the AI cycle.
Our strategic AI testing approach will also address the unique challenges that are put forward by machine learning algorithms. Moreover by adopting all the tools that we mentioned in this article, you can massively improve the productivity of the entire testing cycle.
Ultimately, you must remember that deploying AI at scale is much more than just high accuracy metrics. It’s about accountability, safety, and user confidence. Therefore, you must have a rigorous testing and monitoring ecosystem to protect your organization from legal and reputational risks. This system will also be responsible for empowering teams to iterate rapidly and also set the foundations for responsible AI innovation.
