- Data privacy and security: As AI systems handle sensitive data, there are concerns about data privacy and security. This can limit the availability of data for training and research.
- Data fragmentation: Data is often scattered across different sources, formats, and locations, making it challenging to integrate and utilize effectively.
- Data obsolescence: As AI systems evolve, the data they were trained on may become outdated or irrelevant, requiring continuous updates and retraining.
- Lack of standardization: There is a lack of standardization in data formats, annotation schemes, and evaluation metrics, which can hinder collaboration and reproducibility in AI research.
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