Practice holism and do not reduce our ethical focus to components
We routinely employ statistical analyses to search for unwarranted data, model, and outcome bias.
Ensure fairness
Ensure fairness
Ensure fairness
We are fully determined to combat all types of reducible bias in data collection, derivation, and analysis.
Ensure fairness
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.
Ensure fairness
All data sets are evaluated for fairness, possible inclusion of sensitive data and implicitly discriminatory collection models.
Ensure fairness
All data sets are evaluated for fairness, possible inclusion of sensitive data and implicitly discriminatory collection models.
Ensure fairness
We execute statistical tests to look for imbalance and skewed datasets and include methods to augment datasets to combat these statistical biases.
Ensure fairness
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.