Quality data takes precedence over quantity, allowing for more selective inclusion of personal information and reducing biases. By measuring associations within data, it's possible to identify and address skewed representations, contributing to a more equitable outcome. Efforts like model cards and data cards enhance the understanding of data quality and biases, promoting transparency and accountability in AI development.
