Why does kernel density estimation fail in high dimensions?

Because required sample size grows exponentially with dimensions.

Kernel Density Estimation relies on local neighborhoods to estimate probability density. In high dimensions, neighborhoods become empty due to sparsity, making KDE estimates noisy and unreliable. Bandwidth selection also becomes unstable, leading to oversmoothing or undersmoothing.