What are some examples of data diversity?
Data diversity refers to the variety of data types and sources used in AI training. Examples include demographic data, geographic data, and various formats like text, images, and audio.
Can you give specific examples of diverse datasets?
Sure! Examples include the ImageNet dataset for images, the Common Crawl dataset for web text, and the OpenStreetMap for geographic data.
Why is data diversity important in AI?
Data diversity is crucial because it helps reduce bias, improves model accuracy, and ensures that AI systems are more representative of the real world.
How can organizations ensure data diversity?
Organizations can ensure data diversity by actively sourcing data from various demographics, regions, and formats, and by regularly auditing their datasets for representation.
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