Fair AI Data provides a platform that automatically detects and quantifies bias in structured and synthetic datasets, highlighting intersectional gaps and distinguishing genuine patterns from synthetic distortions. It delivers interactive dashboards, actionable rebalancing recommendations, and a free README Creator for standardized, audit‑ready documentation, helping researchers and data stewards ensure fairness and compliance before model deployment.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations that generate or use synthetic datasets often cannot detect hidden biases, especially intersectional gaps where minority groups are underrepresented or misrepresented. Undetected bias in synthetic data can lead to AI models that reinforce inequities and produce inaccurate outcomes for diverse user populations.
Solution
Fair AI Data offers a platform that analyzes structured and synthetic datasets to surface and quantify bias across multiple protected attributes such as gender, geography, disability, and race. The system highlights both fidelity (accurate patterns) and hallucination (distorted or missing patterns) to show where synthetic data mirrors reality and where it diverges. Users receive detailed reports that document generation methods, identify intersectional gaps, and provide actionable recommendations for rebalancing data. A free README Creator generates standardized documentation to ensure transparency and traceability from data creation through deployment. The platform supports compliance and ethical standards for researchers, data stewards, and open‑science repositories.
Target Audience
Primary users are AI researchers, data stewards, and organizations that produce or curate synthetic datasets, including academic labs and enterprises seeking compliant, unbiased data for model training.
Features
- Automated bias detection that evaluates intersectional fairness across multiple protected classes
- Fidelity vs. hallucination analysis to distinguish genuine data patterns from synthetic distortions
- Interactive dashboards that visualize demographic gaps and suggest corrective actions
- README Creator tool that produces standardized, audit‑ready documentation for dataset provenance
- Guidance resources for depositing synthetic data in open‑science repositories
- Configurable fairness metrics to align with specific regulatory or ethical requirements