Fair anomaly detection and localization

Described are techniques for fair anomaly detection. The techniques include generating an anomaly detection model based on a Gaussian distribution of historical data, a mean vector of the Gaussian distribution, and a precision matrix of the Gaussian distribution. The mean vector and the precision matrix can be generated by reducing a function below a threshold, where the function can include the Gaussian distribution, a first regularization term configured to generate similar anomaly scores for inputs with similar fair features and independent of unfair features, and a second regularization term configured to generate similar anomaly localization scores for the inputs with the similar fair features and independent of the unfair features. The techniques further include inputting a new data to the anomaly detection model and generating an anomaly score and an anomaly localization score associated with the new data based on the Gaussian distribution, the mean vector, and the precision matrix.