What is meant by "model bias" in AI?

Model bias in AI refers specifically to the tendency of a model to reflect and perpetuate the prejudices and stereotypes present in its training data. When algorithms are trained on datasets that contain biased representations—whether related to race, gender, socio-economic status, or any other characteristic—the model can inadvertently learn and reproduce these biases in its predictions or outputs. This results in outcomes that can unfairly favor or disadvantage certain groups, thereby impacting fairness and equity in AI applications.

Understanding model bias is crucial because it can have real-world implications, affecting decisions in areas such as hiring, law enforcement, lending, and healthcare. By addressing these biases, developers can create more equitable AI systems that better reflect the diversity of the population and promote fairness in their applications.