Consider an "AI" that rates the probability of recidivism for prisoners nearing their parole date. That score would then be presented to the parole board, and taken into consideration in determining whether or not to grant parole. If this AI were accidentally/incidentally accurately determining the race of the prisoner, then the output score would take that into account as well. Black men have a recidivism rate significantly higher than other groups[1]. The reasons for the above aside - it's a complex topic, and outside the scope of this analogy - this is extremely undesirable behavior for a process that is intended to remove human biases.
You might then ask, how does this relate to medical imaging? Medical decisions are regularly made based on the expected lifespan of the individual. It makes little sense to aggressively treat leukemia in a patient who is currently undergoing unrelated failure of multiple organs. Similarly it would likely make sense for a healthy 30-year-old to undergo a joint replacement and associated physical therapy, because that person can reasonably be expected to live for an additional 40 years while the same treatment wouldn't make sense for a 70-year-old with long-term chronic issues. This concept is commonly represented as "QALY" - "quality-adjusted life years".
Life expectancy can vary significantly based on race[2].
An AI that evaluates medical imagery that considers QALY in providing a care recommendation may result in a positive indicator for a white hispanic woman and a negative indicator for a black non-hispanic man, with all else being equal and with race as the only differentiator.
In short - it's not necessarily a bad thing for a model to be able to predict the race of the input imagery. The problem is that we don't know why it can do so. Unless we know that, we can't trust that the output is actually measuring what we intend it to be measuring.
1: https://prisoninsight.com/recidivism-the-ultimate-guide/ 2: https://www.cdc.gov/nchs/products/databriefs/db244.htm
