The Overlooked Fairness Trap: Who Gets Missed in AI's Long Tail?

[!success] Key Data Set: In mainstream chest X-ray AI classification models, the miss rate for rare diseases—which account for less than 1% of samples in long-tail distributions—is as high as 31.7%, while the miss rate for common diseases is only 4.2%. Even more unsettling is that when models are adjusted to fixed thresholds for clinical deployment, the miss rates for certain subgroups (such as elderly women or low-contrast images) are three times higher than the overall average.