bias

A bias is a systematic deviation from truth or fairness that can arise in data, models, or decision processes. In statistics, bias typically means the difference between an estimator’s expected value and the true value of the parameter it’s intended to estimate.

In machine learning, algorithmic bias refers to systematic patterns in model behavior that produce unfair or discriminatory outcomes across groups, often stemming from imbalanced data, labels, or design choices.

Practitioners assess such bias with group-fairness metrics like demographic parity and equalized odds. They mitigate biases using better data collection, sample reweighting, fairness-constrained training objectives, and post-processing adjustments to model outputs.