Which of the following is a way to reduce bias in an AI model?

Utilizing diverse training data is a crucial strategy for reducing bias in an AI model because it ensures that the model is exposed to a wide range of examples during training. When a model is trained on a diverse dataset, it can learn more generalizable patterns and relationships instead of becoming biased towards the characteristics of a specific subgroup. Diverse data can include various demographics, scenarios, and contexts, which helps to create a more balanced understanding and improves the model's ability to make informed predictions across different populations.

In contrast, ignoring problematic data points or focusing solely on recent data could exacerbate biases by failing to account for historical data or outliers that might provide valuable insights. Using a variety of data sources is beneficial, but if those sources are not diverse in terms of demographics or scenarios, they may not effectively mitigate bias. Hence, the key to addressing bias lies in ensuring the training data itself is varied and representative of a broad spectrum of real-world scenarios.