AI and ML Error

The rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) brings powerful capabilities, but also significant sources of error that demand careful attention.

Data Bias
This is arguably the most common and pernicious error. If the training data reflects real-world societal biases (e.g., racial, gender), the model will not only learn but amplify them. Garbage in, bias out. Auditing and cleaning training data is paramount.

The Black Box Problem
Deep neural networks are complex, making it difficult to trace why a model made a specific prediction. This lack of interpretability (or explainability) is a huge error source, especially in sensitive domains like finance or healthcare, where accountability is crucial.

Model Brittle-ness (Adversarial Attacks)
ML models can be surprisingly fragile. A small, imperceptible change to an input image (an "adversarial attack") can cause a model to misclassify it completely. This is a critical security and reliability error in real-world deployment.

Overfitting and Underfitting
These classic errors relate to the model's ability to generalize. Overfitting happens when a model memorizes the training data too well, failing on new data. Underfitting happens when the model is too simple to capture the underlying patterns. Finding the balance is essential for accuracy.