In the context of AI and Machine Learning, bias refers to systematic errors or prejudices in the data or the model that lead to unfair or incorrect outcomes. This can manifest as 'data bias' (when training data is not representative of the real world), 'algorithmic bias' (when the model amplifies existing inequalities), or 'inductive bias' (assumptions made by the learning algorithm to generalize). Addressing bias is crucial for developing ethical and reliable AI systems.
The study of bias has roots in statistics and sociology. In AI, it gained prominence as machine learning models started being deployed in high-stakes domains like criminal justice, hiring, and lending, revealing disparities in performance across different demographic groups.
Bias mitigation is now a major area of research and policy. Tech companies and researchers are developing tools and frameworks to detect and reduce bias in datasets and models. It is a central topic in AI ethics and regulation.