Paritii AI Equity Framework and Guide for ML/AI Development

In the age of advancing AI technology, prioritizing equity is crucial.

shape shape


Despite AI's potential to enhance education, there are risks of unintentional bias. This guide offers tools for clear problem definition and stakeholder engagement to mitigate such risks.

Pre Development

The pre-development phase is foundational for cultivating responsible AI practices.

Learn More

Model Development

Ensuring equity in model development requires prioritizing the identification of dependable data sources for predictive modeling.

Learn More

Post Development

The post-development phase of a machine learning project is where the focus shifts from initial development and deployment to ongoing maintenance, optimization, and evaluation.

Learn More

Post Implementation

In the post-implementation phase, the focus shifts to ensuring that the deployed AI system continues to uphold principles of equity, fairness, transparency, and inclusivity prioritized during development.

Learn More