The Diversity Gap in AI
AI models are only as good as the data they're tested on. When evaluation datasets lack diversity, biases go undetected - leading to systems that work well for some users but fail others.
Our diverse datasets help you identify where your models underperform, so you can build AI that works reliably for everyone, not just the majority.
Available Datasets
Image Data
Thousands of professionally photographed images featuring diverse subjects across ethnicities, ages, body types, abilities, and contexts.
- Facial recognition evaluation
- Object detection testing
- Image classification benchmarks
- Skin tone analysis
Audio & Voice Data
Our largest collection - diverse voice samples across accents, dialects, languages, ages, and speech patterns for comprehensive audio AI evaluation.
- Speech recognition accuracy
- Accent & dialect coverage
- Voice assistant testing
- Transcription benchmarks
Why Evaluation Matters
Identify Blind Spots
Discover where your models underperform before your users do. Diverse evaluation data reveals biases that homogeneous test sets miss entirely.
Build Trust
Demonstrate to stakeholders and users that your AI has been rigorously tested across diverse populations - not just the majority.
Reduce Risk
Catch fairness issues in development, not production. Proactive evaluation is far less costly than reactive fixes after launch.
Ethically Sourced
All content comes from consenting creators who are fairly compensated. Use our data knowing it was obtained responsibly.
Diverse images available
Audio samples across accents
Creator-consented content