Your comment reads like "company X should not be shipping products with issues affecting users" - as long as it was not intentional and they take corrective actions, things are working as well as they can.
At this point it's well known that unfair biases can easily creep into these kind of systems. I don't think it's unreasonable to require products that could potentially cause much harm (e.g. online censoring, scoring CVs, vetting loan applications) to be tested for fairness before letting them loose on the public.
> The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates.
Wouldn't we need to know the true rate of abusive tweets by race in order to conclude this? Is this addressed at all, e.g. with human labelled data, or am I mistaken in thinking this is necessary?
[1] https://fortune.com/2019/08/16/google-jigsaw-perspective-rac...