This is a rationalization that fairly easy to debunk (in two ways, even):
1) Gorillas do not have dark skin the way that humans have dark skin. In fact, many features differentiate gorillas from all humans, including dark-skinned ones. The bias present in the model has to overcome these differences to categorize dark-skinned humans with gorillas rather than light-skinned humans. It's reasonable to question how this could happen accidentally.
2) The contention is that the data was poisoned. How, exactly, do you tell apart a case where not enough black people were included in the training data on purpose, versus as a result of institutional racism? In fact, does it matter? One could hold that the data poisoning simply happened at a different part of the process.
Also, this:
>with a human racial composition that reflects the makeup of the population of the first world, i.e. the majority have light skin.
is arguable. The "first world" (within the US/Western political sphere of influence) includes billions of people who would be considered brown/black, including black, indigenous, and mestizo South and North Americans, black Africans, inhabitants of select West Asian countries, East Asians in Japan, and indigenous inhabitants of Oceania. What you mean to say is the "a human racial composition that reflects the makeup of populations Google engineers deem important," which is again damning in its own way.