What can result from training machine learning models with inadequate data?

Training machine learning models with inadequate data can lead to bias and discrimination in predictions. When a model is trained on a limited dataset, it may fail to capture the full diversity and complexity of the data it will encounter in real-world applications. This lack of representation can cause the model to make assumptions based on the limited examples it has seen, which may not reflect the true characteristics of the broader population.

As a result, the model may exhibit biased behavior, where certain groups or features are underrepresented, leading to discrimination in its predictions. For instance, if a model is trained primarily on data from one demographic group, it may perform poorly or inaccurately when applied to individuals outside that group, reinforcing existing inequalities.

Ensuring that a training dataset is comprehensive and representative is crucial for developing a fair and effective machine learning model. This approach helps mitigate biases, allowing for more equitable outcomes when the model is deployed in practice.