What does "data bias" refer to in AI development?

Data bias refers to the issues that arise when the data used to train AI systems is not representative of the real-world population or situations it is meant to serve. This can manifest as underrepresentation or misrepresentation of certain demographic groups, leading to models that perform poorly for those groups.

In AI development, if a dataset lacks diversity or includes skewed perspectives, the resulting AI system might have a biased understanding and produce inequitable or unfair outcomes. For example, if facial recognition software is trained predominantly on images of lighter-skinned individuals, it may struggle to accurately identify individuals with darker skin tones. Thus, the correct answer highlights the critical importance of ensuring that the datasets are representative to create fair and effective AI systems, making it a fundamental concern in AI ethics and responsibility.