We routinely employ statistical analyses to search for unwarranted data, model, and outcome bias.

We are fully determined to combat all types of reducible bias in data collection, derivation, and analysis.

Our teams are trained to identify and challenge biases in our own decision making and in the data we use to train and test our models.

We execute statistical tests to look for imbalance and skewed datasets and include methods to augment datasets to combat these statistical biases.

This review includes in sample and out of sample testing to mitigate the risk of model overfitting to the training data, and biased outcomes in production.