TechAIDBlogComplexities in Testing AI Unveiled: Navigating the 7 Shades of Grey
By Alex Dillon 06/03/2023 0

Complexities in Testing AI Unveiled: Navigating the 7 Shades of Grey

In a recent article by Jason Arbon, he highlighted the need for improved AI testing, challenging testers to address the gaps brought about by this complex technology. As CTOs and software testers, it is crucial to delve into the intricacies of testing AI and understand the critical differences between AI testing and traditional application testing. Taking on that challenge, I’d like to address many of the complexities in testing AI that your company and your testers must handle to adapt to this new world that we are in — the grey areas found in trying AI models vs. testing traditional applications.

Complexities in Testing AI

First, it is essential to understand that understanding written and non-written requirements drives traditional app testing. Testers identify risks and bugs based on these requirements and business priorities, with the definition of a ‘bug’ often changing according to those priorities. However, in AI, the concept of a ‘bug’ takes on a new dimension, expanding beyond mere functionality and delving into the realm of data and learning capabilities. This concept means that bugs are no longer just tied to functionality but rather to the ability of the AI model to learn from the data, introducing much variability and many probabilities that cannot be predicted.

Differences between AI testing and traditional app testing

The main difference between traditional software testing and AI model testing lies in the subject matter of the tests. While software testing primarily focuses on preventing bugs in the code, AI model testing must also scrutinize the data and the model itself, ensuring that the AI model performs as expected. This introduces a level of complexity not found in traditional software testing, as testing an AI model involves more than just observing a specific behavior but also ensuring the smooth functioning of the learned logic of the model.

Furthermore, it is essential to understand that traditional software is deterministic, leaving no room for misunderstanding what is a bug for a particular organization at a specific point in time. However, AI models do not possess such a deterministic structure. These models aim for a realistic 70–90% accuracy rate, introducing a broad spectrum of potential outcomes, starkly contrasting traditional software.

This reality creates complexities in testing AI that weren’t present in traditional app testing and, therefore, grey areas that must be addressed with different testing methodologies.

Grey Areas in AI Testing

 

Model Robustness

To start, let’s look at “Model robustness,” which is a notable grey area in AI testing.

In traditional software testing, robustness typically relates to how well an application handles errors or unexpected inputs. For instance, if you enter invalid data into a form on a website, you would expect the site to handle it gracefully, perhaps by displaying an error message. This is generally straightforward to test: you define a set of unexpected inputs, submit them to the application, and verify that it responds correctly.

In contrast, the robustness of an AI model refers to its ability to provide reliable outputs in the face of varied and potentially unpredictable inputs. This could include new data that differs from the training data or adversarial inputs designed to fool the model.

This becomes a “grey area” because, unlike traditional software, it’s unclear what the “correct” output should be in these scenarios. AI models deal with probabilities and uncertainties and may have multiple plausible outputs for a given input. Defining the boundaries of acceptable behavior and designing tests that adequately cover the potential input space can be challenging.

Furthermore, improving an AI model’s robustness often involves a trade-off with accuracy. A more robust model may be less accurate on its training data, and vice versa. This adds another layer of complexity to the testing process.

 

Continual Learning and Model Drift

Continual Learning is the ability of an AI model to learn and adapt over time to new data. This desirable feature allows models to improve performance and stay relevant as the world changes. However, it also poses a challenge for testing. Unlike traditional software, which remains static unless explicitly updated, a continual learning model constantly changes. This means the model must be continually re-tested to ensure it continues to perform as expected. There’s also the challenge of determining whether changes in the model’s behavior are due to legitimate learning or indicate a problem that needs to be addressed.

Model Drift refers to the phenomenon where an AI model’s performance degrades over time because the data it encounters in the real world diverges from the data it was trained on. This is a common issue in many real-world AI applications, as the world constantly changes. Detecting and mitigating model drift is crucial to maintain the performance of an AI model over time. However, like with continual learning, it introduces a “grey area” in testing. It’s not always clear what constitutes an unacceptable level of drift, and it’s often challenging to isolate the cause of the drift and determine how to address it.

These factors introduce a level of uncertainty and complexity that contrasts with the more static nature of traditional software testing. They require continuous monitoring, re-evaluation, and potentially re-training of the AI model, less defined and more complex processes than traditional software testing methodologies.

 

Explainability and Transparency

Explainability and Transparency are two critical concepts in AI that introduce further complexity into the testing process, making it substantially different from traditional software testing.

Explainability refers to the ability to understand and interpret the decision-making process of an AI model. Because AI models, particularly deep learning models, are often considered “black boxes,” they can make predictions without providing a clear, understandable reason for their decisions. This lack of explainability introduces a grey area in testing because it can be challenging to determine why the model makes certain decisions and whether they are correct or appropriate. This contrasts with traditional software, where the logic is explicitly programmed and can be examined directly.

Transparency, on the other hand, is about making the inner workings of the AI model and the processes around it understandable to stakeholders. This includes the data used to train the model, the training process, and the model’s decision-making process. Testing for transparency can be complex because it requires a holistic view of the entire AI system, including aspects that might not directly relate to the model’s performance, such as data collection and processing procedures.

These factors contribute to the “grey area” in AI testing. They require a broader, more profound approach to testing beyond simply evaluating the model’s output for a given input. This adds a complexity layer not typically found in traditional software testing.

 

Fairness and Bias

Fairness and Bias are additional considerations that introduce complexity into the testing of AI models, distinguishing it from traditional software testing.

Fairness in AI refers to the concept that AI models should make decisions that do not unfairly discriminate against certain groups. For instance, an AI model used for hiring should not favor candidates of a particular gender, ethnicity, or other protected characteristics. Testing for fairness requires analyzing the model’s decisions across different demographic groups and ensuring it performs equally well for all of them. This complex task requires careful design of testing procedures and a deep understanding of the model and the data it’s trained on.

Bias in AI models can be a significant issue that undermines their fairness. AI models learn from the data they’re trained on, and if that data contains biases, the model is likely to learn and replicate them. For example, suppose a model trained on historical hiring data learns that candidates of a particular gender were less likely to be hired. In that case, it may unfairly discriminate against candidates of that gender. Testing for bias involves examining the training data and the model’s decisions to identify and correct any unfair biases.

These factors further contribute to the “grey area” in AI testing. They require a nuanced understanding of the AI model’s technical aspects and the societal context in which it operates. This contrasts with traditional software testing, which primarily focuses on the technical correctness of the software and does not typically involve considerations of fairness and bias.

 

Lack of Clear Pass/Fail Criteria

The Lack of Clear Pass/Fail Criteria is another aspect that adds complexity when testing AI models, distinguishing it from the testing of traditional software applications.

In traditional software testing, determining whether a test has passed or failed is usually straightforward. Testers typically have explicit expectations for what the software’s output should be for a given input, and if the actual output matches the expected output, the test passes.

However, defining pass/fail criteria can be far more complex in AI testing. This is because the “correct” output for a given input is not always clear-cut. For example, consider an AI model that generates captions for images. What constitutes a “correct” caption can be somewhat subjective and vary depending on the application’s requirements.

Moreover, AI models are probabilistic, meaning they do not always produce the same output for a given input. This inherent uncertainty further complicates the definition of pass/fail criteria.

Additionally, the performance of AI models is often evaluated based on statistical measures over a set of test examples rather than individual pass/fail tests. For example, a model might be evaluated based on its accuracy, precision, recall, or other metrics across a test set. This requires a different approach to testing than traditional software.

Thus, the lack of clear pass/fail criteria introduces a significant “grey area” in AI testing, making it fundamentally different and more complex than traditional software testing.

 

Data Dependencies

Data Dependencies represent another critical challenge in testing AI models, setting it apart from traditional software testing.

AI models fundamentally differ from traditional software in learning from data rather than being explicitly programmed. This means the quality and characteristics of the data used to train and test the model can significantly impact its performance.

In traditional software testing, the software’s behavior is dictated by the code written by developers, and it will behave the same way given the same inputs, regardless of external data. But in AI, the model’s behavior is learned from data, and thus the choice of data for training and testing is crucial.

Data dependencies in AI testing can introduce several challenges:

  1. Quality of data: The data used to train and test the model must be of high quality. It must be accurate, relevant, and free from errors and biases. Poor quality data can lead to a poorly performing model, even if the model’s architecture is sound.
  2. Representativeness of data: The data must represent the situations the model will encounter in the real world. If the data does not reflect the real-world distribution of inputs, the model may perform poorly when deployed.
  3. Data privacy and security: Data used in AI often includes sensitive information. Ensuring this data is used responsibly and securely is essential to AI testing.
  4. Changing data: In many applications, the data distribution can change over time, a phenomenon known as concept drift. This can cause the model’s performance to degrade if it is not retrained or adapted to the new data.

Therefore, data dependencies add a layer of complexity to AI testing, contributing to the “grey area” that does not exist in traditional software testing.

 

Complexity of AI Models

Finally, the Complexity of AI Models is another factor that distinguishes AI testing from traditional software testing and adds to its grey areas.

AI models, particularly deep learning models, can be extremely complex, with potentially millions or even billions of parameters. This complexity can make these models behave in ways that are difficult to predict and understand. This starkly contrasts traditional software, where the programmer’s code explicitly defines the behavior.

Here are some aspects of AI model complexity that pose challenges for testing:

  1. Black Box Nature: AI models, particularly neural networks, are often considered “black boxes” because their internal workings are difficult to interpret. This makes it hard to understand why a model makes a particular decision, complicating the testing process.
  2. Non-deterministic Behavior: AI models, unlike traditional software, can exhibit non-deterministic behavior. Depending on factors like the initial random weights in a neural network, they might produce different outputs for the same input. This unpredictability makes it difficult to design effective tests.
  3. Overfitting: AI models can overfit to their training data. This means they perform well on the training data but poorly on new, unseen data. Detecting and preventing overfitting is a significant challenge in AI testing.
  4. Sensitivity to Small Changes: AI models can sometimes be sensitive to minor changes in input data, a problem known as “adversarial vulnerability.” This can lead to drastically different outputs for almost identical inputs, complicating the testing and validation process.
  5. Complex Interactions: AI models can have complex interactions between their components (such as layers in a neural network). These interactions can be challenging to understand and test thoroughly.

 

How can Software Testers address these grey areas?

The complexities outlined above highlight the need for a paradigm shift in software testing approaches when it comes to AI. They emphasize the requirement for innovative testing strategies and methodologies specifically designed to address the unique challenges posed by AI. While these grey areas undoubtedly present challenges for software testers, there are strategies we can employ to tackle them effectively, such as the following:

  1. Implement Robustness Testing: Techniques like fuzzing and adversarial testing, where unexpected or deliberately challenging data is inputted, can help uncover vulnerabilities and increase the robustness of AI models.
  2. Employ Continual Monitoring and Updating: AI models can change over time. Setting up systems to continually monitor the performance of AI models can help testers spot and correct shifts, ensuring the model stays accurate and relevant.
  3. Utilize Explainability Tools: AI often seems like a “black box.” By using AI explainability tools, testers can better understand how models are making decisions, which can improve transparency and trust in the models.
  4. Focus on Bias Detection and Mitigation: Fairness and bias are critical in AI testing. Implementing bias detection methods, scrutinizing training data for bias, and applying mitigation techniques can help ensure that AI models are fair and unbiased.
  5. Apply Data Validation Techniques: The quality and representativeness of the data used in AI is crucial. Testers can employ data validation techniques to ensure that the data feeding into the models is accurate and appropriate.
  6. Manage Model Complexity: AI models can be complex. Techniques like simplifying model architecture, using regularization techniques to prevent overfitting, and employing interpretability techniques can help testers understand and manage this complexity.
  7. Collaborate with AI Specialists: AI testing requires specialized knowledge. Building relationships with AI experts can give testers valuable insights and expertise, helping them design more effective tests and better understand AI behavior.
  8. Ensure Compliance with Ethics and Regulations: Testers should ensure that AI models comply with ethical guidelines and regulations. This includes considering aspects such as fairness, privacy, and transparency.

While these strategies can help, it’s important to note that the field of AI testing is still developing. As we continue to embrace the possibilities of AI, we must also equip ourselves with the knowledge and skills to ensure these powerful tools function as intended. After all, the future of AI is not just about what it can do but also about how we can effectively and responsibly manage its capabilities. Therefore testers should constantly learn and adapt to AI’s new challenges in the software testing landscape. In this rapidly changing landscape, it’s essential to navigate the complexities of AI testing with care and consideration.

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