What are the challenges of labeled data in AI?

What are the challenges of labeled data in AI?

Labeled data is crucial for training AI models, but it comes with several challenges. These include the high cost of labeling, the need for expert knowledge, and potential biases in the data.

Can you elaborate on the cost aspect?

Certainly! Labeling data can be expensive, especially for large datasets. It often requires hiring skilled annotators or using specialized tools, which can significantly increase project budgets.

What about biases in labeled data?

Biases can arise if the labeled data does not represent the real-world scenario accurately. This can lead to AI models that perform poorly or unfairly in certain situations.

How can we mitigate these challenges?

Mitigating these challenges involves using techniques like active learning, data augmentation, and ensuring diverse representation in the training data.