👉 Bias math, often used in machine learning and statistics, refers to the systematic errors or distortions that can occur when models learn and replicate biases present in their training data. These biases can stem from various sources, such as historical prejudices, underrepresentation of certain groups, or skewed data collection methods. In essence, if the training data reflects these biases, the model will likely perpetuate and even amplify them in its predictions or decisions. For example, a facial recognition system trained predominantly on images of one ethnicity might perform poorly on others, reflecting the dataset's inherent bias. Addressing bias math involves techniques like data preprocessing to balance datasets, algorithmic adjustments to mitigate learned biases, and continuous monitoring and evaluation to ensure fairness and equity in model outcomes.
