The Most Important Work in AI Training Is Also the Most Overlooked

The quality of data used in AI training is crucial for the effectiveness and reliability of AI models, particularly in high-stakes applications such as healthcare and public policy. Despite this, data work is often overlooked and undervalued, leading to "data cascades" that can have severe consequences on model performance. Addressing these issues requires prioritizing data quality, providing adequate training for data collectors, and recognizing the importance of domain expertise in high-stakes AI projects.