> However, just from a cursory review of the mugshots, people of color were disproportionately represented in the mugshot database. So it’s not entirely fair to criticize the facial recognition technology for matching more people of color.
No, the point is the disproportionate representation, and it is a fair criticism, because it is a fundamental limiting factor in the use of the technology. You seem to be distinguishing the "technology" from the data source, but that is not possible. The quality of the technology depends on the quality of the data provided to it.
> I believe a curated list of mugshots with certain characteristics would result in a similar representation of mismatches. Nothing I have read about the technology suggests that there are inherit [sic, is inherent] bias.
This bias is in the data, not the algorithm per se. This is quite accepted in many, even most, criticisms of machine learning applications in the social sphere. It comes up a lot for example in NLP models, for example with assumptions on gender for professions.
It is a technology problem because it demands a technological solution, because removing any and all bias by hand-curating datasets is just not a scalable approach.