10 Challenges Of Implementing AI In Quality Assurance

Exploring AI in QA, this article addresses ten challenges, from complexity and data quality to ethical considerations, advocating for a balanced AI-human approach.

Margarita Simonova
Margarita Simonova
Founder and CEO of ILoveMyQA

March 22, 2024

8 min read

10 Challenges Of Implementing AI In Quality Assurance

We’ve all read the hyperbolic headlines surrounding AI—that it will revolutionize all aspects of our lives, both personal and professional. While we cannot know ultimately how much transformation will occur due to AI, it is clearly a force to reckon with and is already having an impact on the field of quality assurance (QA).

It’s true that QA can be a challenging endeavor for any organization. We often associate it with high costs and time-consuming manual labor. Effective QA is essential for a business to maintain its customer base because it keeps its reputation for reliable products intact.

Understanding The Challenges Of AI For QA

New solutions such as AI should always be investigated to improve QA processes. Implementing AI for QA comes with many challenges, though. In this article, we’ll take a deep dive into 10 of these challenges that I have identified based on my experience.

1. Complexity

The first challenge to talk about when implementing AI for QA is complexity. AI is often considered to be a “black box,” which means that the inner workings of AI can be a mystery—even to its creators. These models typically consist of millions of parameters that cannot be easily interpreted by people. Because of this, the output it produces can sometimes be difficult to understand and troubleshoot, should issues occur.

One way to address this is to implement models that have some level of transparency. For example, some models include attention maps or feature importance scores that can help give you an idea about what led to the output you are receiving. If you are unsure why you are getting the output you are, try looking for those features.

2. Data Dependency And Quality

An AI model is only as good as the dataset used to train it. So, the training data must be evaluated. This can be accomplished by requesting information about the training data. Checking samples of this data can help determine its quality.

A related concern deals with privacy. Data used to train AI should be gathered with consent from users and any personally identifiable information should be stripped out of it. If this type of sensitive information is in your output from AI, it could represent a liability with privacy laws.

3. Integration With Existing QA Processes

To start integrating processes, the first step is to prepare data. AI models need large, high-quality datasets. However, most organizations have an insufficient amount of unstructured data that also needs cleaning and labeling. Gathering this data for integration can be a time-consuming task and will require expertise in converting the data to the desired format.

4. Skill Gaps And Training Needs

AI may be a mystery to your current employees, and even though AI aims to present human-readable content, getting the most out of the technology requires training to be aware of the software principles behind it.

This type of training can be achieved through a systematic approach. First, skill gaps should be identified, which can occur through assessment tests. Next, training programs should be developed to address identified needs. Training can be delivered through many formats, including online courses, periodic workshops and mentorship programs.

5. Explainability And Transparency

One way to approach this is to use AI models that utilize decision trees or rule-based systems. These systems are clearer in how they reach their conclusions. There are also libraries that help explain a model’s decision-making, such as SHapely Additive exPlanations (SHAP) and Local Interoperable Model-Agnostic Explanations (LIME). With these tools, we can gain better insight into the workings of AI.

6. Cost Implications

One large expense is the price of acquiring or developing the right AI tools. This can involve paying for expensive systems that can handle the intensive data training needed to create AI models. It also includes the infrastructure needed to support AI, such as AI platform licenses, which can vary based on the platform used and whether it is used via a recurring subscription or by usage.

7. Maintaining AI Systems

It isn’t enough to just set up an AI system and expect it to meet all your future needs. We also need to consider the maintenance required. One element to consider is the freshness of the training data. The data will become less relevant over time, so the system needs to be amenable to adapting to new data.

Another aspect to consider is testing scenarios. Since AI system performance can degrade over time, it is important to monitor its performance through testing. Regression tests can be used to check if the system’s performance has not degraded, while stress testing can be used to test the stability and responsiveness of the system.

8. Ethical And Legal Considerations

Biases can be a legal issue if they cause an organization to violate regulations, such as anti-discrimination laws. That can make the organization legally responsible for the results of AI.

AI can also be related to legal considerations if it fails to detect critical issues, such as including material for training that is protected by intellectual property rights. Additionally, training data could include personal data that is protected by data laws such as the GDPR or CCPA.

9. Testing AI Systems

This meta-challenge includes techniques such as adversarial AI and mutation testing. Adversarial AI tests AI by creating output that has been modified to fool the AI, which can help identify weaknesses in a model. Mutation testing is used to generate small changes to test cases to see how models react to unexpected inputs.

10. Balancing AI And Human Insight

Lastly, a final challenge to implementing AI in QA involves balancing AI with human insight. It’s important to find the right balance between AI-driven automation and human intuition. This ensures that while AI streamlines processes and detects patterns, human judgment still allows contextual understanding and nuanced decision-making to enhance the QA process. One strategy to consider is benchmarking, which compares the output of a model to the output of human experts.

Conclusion

While there are challenges to implementing AI in QA, the future of QA is headed towards more AI usage. This means that addressing these challenges is crucial. By using human insight, we can interpret complex, ambiguous AI outcomes and make good judgment calls when using AI.

Margarita Simonova
Margarita Simonova
Founder and CEO of ILoveMyQA

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