Possible. But there's still potential problems with that, tying in to the article's main issue of potential feedback loops/bias in ML algorithms. Let's assume pay is correlated to perf/job title, and members of group X are consistently rated 80% of what a member of group Y would earn for identical performance by unintentionally biased managers. Let's assume that they're all similarly 80% as likely to be promoted given identical performance. Anyone looking at the data would find that pay for X and Y members is fair given their perf scores/job titles, and that members of X tend to underperform compared to Y. They could suspect bias in perf from that, or they could conclude that members of X are fairly paid but statistically underperforming. An objective evaluation of a biased/unfair dataset doesn't necessarily guarantee a fair/objective outcome.