Strategies for diverse data collection

Fair AI systems need training data that represents all users. When data lacks diversity, AI works worse for underrepresented groups.

Here are practical strategies for diverse data collection:

• Audit your existing data to find gaps. Compare this to your target users to set clear representation goals. For new data collection, use stratified sampling to ensure all key groups are properly represented.
• Partner with community organizations to reach underrepresented groups, paying them fairly and explaining how their data will be used.
• When resources are limited, consider synthetic data generation, using algorithms like Generative Adversarial Networks (GANs) to create artificial but realistic examples that match the characteristics of underrepresented groups without requiring additional data collection.
• Apply data augmentation techniques to expand your existing data. For images, this means creating variations by changing brightness, contrast, or angle. For voice data, it includes adding background noise or altering speed. For text, it involves paraphrasing or translating content while preserving meaning.
• Add quality checks throughout your process: regularly measure demographic proportions, test performance across different groups, and set minimum representation requirements before training your model.
• Document your data collection methods and who is represented in your dataset using standard formats like model cards. This documentation helps everyone understand who is included in your training data.