Which option is NOT listed as a pitfall of AI/ML in virtual inspection methods?

• A. Data bias
• B. Overfitting
• C. Lack of explainability
• D. Real-time performance constraints

Answer: (D)

Real-time performance constraints are not an inherent pitfall of the AI/ML approach itself in virtual inspection. They’re an engineering or deployment requirement—how fast the system must process data, the latency limits, and whether hardware and software pipelines can meet those demands. These constraints can be addressed through optimization, hardware upgrades, or redesigning the data flow, so they’re more about system engineering than a flaw in the AI model.

In contrast, data bias, overfitting, and lack of explainability are classic AI pitfalls. Data bias occurs when training data don’t represent the real world well, leading to biased or inaccurate results in new inspections. Overfitting happens when the model learns noise or peculiarities of the training set instead of the true signal, so performance drops on new data. Lack of explainability means the model’s decisions are opaque, making it hard to validate, trust, or justify results in inspection scenarios where understanding the rationale is important. Because these issues directly undermine the model’s accuracy, generalization, or trust, they are recognized as pitfalls of AI/ML, while real-time performance is a deployment-layer constraint.