What could result from underrepresentation in training datasets?

Underrepresentation in training datasets can lead to skewed outcomes based on biased data, which is why the selection of this option is crucial for understanding the implications of dataset composition. When specific groups or data types are underrepresented, the model may not learn to recognize or reflect the characteristics and behaviors of those groups accurately. This can result in biased predictions that favor the majority or more represented data, leading to inaccuracies and potentially harmful outcomes in the deployment of machine learning models.

For instance, if a facial recognition model is primarily trained on images of individuals from one demographic, it may struggle to accurately identify or recognize individuals from other demographics. This not only impacts the performance of the model but also raises ethical concerns regarding fairness and equity. Acknowledging and addressing these biases is essential to ensure that AI systems are effective and equitable for all users, making the underrepresentation in datasets a significant issue to be addressed in AI development.