Performance Fairness
Performance fairness (PF) is about controlling the performance of machine learning models on different subgroups. This allows customers to ensure that their algorithms can deal with diverse inputs in the form of human characteristics or features and function fairly with a high level of accuracy
Performance Fairness
Our PF toolbox allows the creation of synthetic datasets that can be used by customers to train decision models satisfying balanced performance constraints on any combination of subgroups.
Control your performance
Monitor and react to variations in performance between varying sensitive features within your algorithms.
Healthcare
The Fairgen toolbox equips healthcare organisations with novel tools to empower fair decision-making, increase transparency, allow for fair allocation, and improvements in patient outcomes.
Financial Services
The Fairgen toolbox equips financial institutions with novel tools to empower fair decision-making, increase transparency, reducing risk, and opening new revenue channels.
