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#222 [Radar Recap] Scaling Data Quality in the Age of Generative AI
DataFramed
Quality of Data in AI
The speaker discusses the importance of data quality in AI applications and raises the question of whether no data is better than bad data. They emphasize the challenge of defining what 'good' data looks like and how changes in the data state over time can pose difficulties. The speaker highlights the significance of detecting and addressing data issues before downstream users are affected to maintain or rebuild trust in AI systems.
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