Parity AI

Parity AI is a dedicated algorithmic fairness and bias testing platform designed to help organizations identify, measure, and mitigate bias in their AI and machine learning systems. Founded in 2020 and headquartered in New York, New York, Parity AI was built with the singular mission of making AI systems fairer and more equitable, addressing one of the most pressing challenges in responsible AI deployment. The platform provides an intuitive interface for conducting bias audits across the entire machine learning lifecycle, from training data analysis through model development to production monitoring. Parity AI's approach centers on making fairness testing accessible to a broad range of stakeholders, not just data scientists. Business analysts, compliance officers, and product managers can use the platform to run bias assessments without deep technical expertise, thanks to a guided workflow that walks users through defining protected attributes, selecting appropriate fairness metrics, and interpreting results. Parity AI supports a comprehensive set of fairness metrics including demographic parity, equalized odds, predictive parity, and calibration across groups. The platform can analyze both structured and unstructured data, and provides detailed reports that map findings to regulatory requirements under frameworks such as the EU AI Act, NIST AI RMF, and EEOC guidelines. These audit reports are designed to be presentation-ready for regulators, board members, and external auditors. A key differentiator for Parity AI is its focus on actionable remediation. Rather than simply flagging bias, the platform provides concrete recommendations for mitigation, including pre-processing techniques for training data, in-processing constraints for model training, and post-processing adjustments for model outputs. This end-to-end approach helps organizations move from bias detection to bias resolution efficiently. Parity AI primarily serves mid-market organizations in financial services, insurance, healthcare, and human resources, where algorithmic decision-making has direct impacts on individuals and regulatory scrutiny is high. The platform's cloud-based architecture enables rapid deployment, and its API-first design allows integration into existing CI/CD pipelines for automated fairness testing.