End-to-end Machine Learning Pipeline That Ensures Fairness Policies

In consequential real-world applications, machine learning (ML) based systems are expected to provide fair and nondiscriminatory decisions on candidates from groups defined by protected attributes such as gender and race. These expectations are set via policies or regulations governing data usage and decision criteria (sometimes explicitly calling out decisions by automated systems).
People Impacted
$ 156B
Potential Funding
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the problem
Nature and Context

Manually understanding the policies and ensuring fairness in opaque ML systems is time-consuming and error-prone, thus necessitating an end-to-end system that can: 1) understand policies written in natural language, 2) alert users to policy violations during data usage, and 3) log each activity performed using the data in an immutable storage so that policy compliance or violation can be proven later.

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