Summary:
- This article discusses the systematic gap in AI training data, which is a critical issue in the development of accurate and unbiased AI systems.
- The article explains that the data used to train AI models often lacks diversity and representation, leading to biases and inaccuracies in the AI's outputs.
- The article highlights the importance of addressing this gap by actively seeking out and incorporating diverse data sources to ensure AI systems are fair and inclusive.