AI and ML models are trained on large datasets with the purpose of making these models capable of executing tasks on unseen data based on the patterns they identify and learn in the training data.

Underfitting occurs when a model does not learn the structure and patterns in the data . The model fails to capture the underlying complexity and performs poorly when put to test. The model exhibits a high bias and  consistently makes incorrect predictions(low variance).

When a model is said to be underfitting, it could be so for a host of reasons.

The architecture of the model might be oversimplified or there could be insufficient features (variables) in the model. The model could also have been inadequately trained . A model could also end up underfitting when it is excessively regularized in an attempt to avoid overfitting. 

Some examples of underfitting models are of recommendation systems presenting irrelevant suggestions, chatbots giving generic responses . Underfitting can mislead users and reduce trust in AI systems and optimally addressing it remains a key focus area for engineers. 

Underfitting can at times be mistaken for poor training data. Developers may also on occasion  prefer an underfitting model, over an overfitting one.  Achieving this balance between simplicity and accuracy remains central to building better performing AI systems .