Blind Justice: Fairness with Encrypted Sensitive Attributes
This addresses privacy and fairness concerns in AI for users and developers, though it is incremental as it builds on existing secure computation techniques.
The paper tackles the problem of training fair machine learning models without revealing sensitive attributes like gender or race, by introducing secure multi-party computation methods that encrypt these attributes, enabling fair model learning and verification without disclosure.
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive attributes should not be considered. On the other hand, in order to avoid disparate impact, sensitive attributes must be examined, e.g., in order to learn a fair model, or to check if a given model is fair. We introduce methods from secure multi-party computation which allow us to avoid both. By encrypting sensitive attributes, we show how an outcome-based fair model may be learned, checked, or have its outputs verified and held to account, without users revealing their sensitive attributes.