AI Goggles vs Beer Goggles

Gender imbalanced datasets may affect the performance of AI pathology classification
May 2020, phys.org

This offers a very simple way to understand bias in neural networks. First, an explanation from the press article: In recent years, researchers have found that AI apps used to approve mortgage and other loan applications are biased, for example, in favor of white males. This, researchers found, was because the dataset used to train the system mostly comprised white male profiles. In this new effort, the researchers wondered if the same might be true for AI systems used to assist doctors in diagnosing patients. 

And their findings: In looking at their results, the researchers found that there was a definite bias—when the data was mostly male, the error rates for processing female profiles rose. The same was true if the ratios were reversed. They also found that over-representing one gender or the other did not confer an advantage—the error rates remained relatively stable.

Post Script
Samus was a woman.