What causes false negatives in AI systems?

What causes false negatives in AI systems?

False negatives occur when a system incorrectly identifies a negative result. This can happen due to various factors like insufficient training data, model bias, or threshold settings.

Can you give me an example of a false negative?

Sure! In medical testing, a false negative would mean a test indicates a patient does not have a disease when they actually do.

How can we reduce false negatives in AI?

To reduce false negatives, you can improve data quality, adjust model thresholds, and use more comprehensive training datasets.

Are there specific industries where false negatives are more critical?

Yes, industries like healthcare, security, and finance are particularly sensitive to false negatives due to the potential consequences.