Which of the following is a common challenge of neural networks?

• A. Low computational cost
• B. Overfitting
• C. Easy interpretability
• D. Lack of interpretability

Answer: (D)

Neural networks are often described as black boxes: decisions come from complex interactions across many layers and countless parameters, making it hard to trace exactly why a particular output was produced. This opacity is especially problematic in risk management and regulatory contexts where you need to explain, justify, and audit decisions, understand which input factors drove a risk score, and monitor for biased or drifted behavior. Because of this distributed, non-linear reasoning, interpretability tends to be lacking by design, which makes it a recurring difficulty across many neural network applications.

Overfitting can happen when a model has too much capacity relative to the amount of data, but that concern is more about generalization performance than about the inherent openness of the model. Low computational cost is not typical for neural networks, which usually require substantial computing resources. And easy interpretability would be a strength, not a challenge. So the lack of interpretability best captures a fundamental challenge associated with neural networks.