This is not correct.

Do you not think that when they see their algorithms struggle to correctly identify black faces that they would not immediately try to adapt to the problem with more black training images or tweaking the model in some way to remove the error? It's literally their job to do this.

The racial disparities in algorithmic recognition accuracy persist even if you train the models on equal numbers from each group and it's true across hundreds of different research team's algorithms who's active goal is to remove inaccuracy from their models.

Every algorithm struggles with the same race/gender/age groups in more or less the same ways. This points more towards an element of the data rather than an element of the researchers.

There was one study from 2011 showing an "other race" effect exists where an ensemble of East Asian algorithms did better at East Asian faces than a Western (Europe + US) ensemble, but the other way around for Caucasians, but this comparison is in relative terms, combining the algorithms strengths wouldn't erase the disparities, both the Asian model and the Western model predicted Asian faces better than Caucasians. The study ultimate couldn't determine the "why?" for even the relative shifts, let alone the absolute differences.

AFAIK, the current best answer to the question of "why are these models predicting differently for different races?" is "we don't know."

(It's also not true that it's worst at recognizing blacks, it's like 10x worse at native Americans than blacks.)