What can cause bias in AI models?

Bias in AI models can stem from having unbalanced or non-representative training data. When the data used to train an AI model does not accurately reflect the diversity of the real-world scenarios it will encounter, the model will likely develop skewed or biased traits. For example, if certain groups are overrepresented while others are underrepresented, the AI may perform well for the larger group but poorly for the smaller ones. This disparity can lead to predictions or decisions that unfairly favor one group over another, thus creating a system that does not produce equitable outcomes.

Using diverse training data and balanced, representative datasets—highlighted in the other choices—are methods aimed at mitigating bias. These strategies promote fairness and inclusivity in AI applications, ensuring that models generalize better across different populations. Using a large variety of data sources can also aid in creating a more robust model, but if this data is not carefully curated to avoid imbalance or bias, it may not effectively solve the problem.