This does look like a clear misinterpretation/misuse of statistics, and a blemish on the otherwise fine article.

Suppose we have a perfectly reliable test for tuberculosis, but we foul it into having an equal false positive or negative rate as follows: we perform the perfect test, the flip a fair coin, and if it is "heads" we invert the test's outcome.

Then if we apply the test to a population in which the incidence of tuberculosis is low (which is actually the case), most of the false results will be false positive.

In other words, a high false positive rate in a screening test relative to a low false negative rate does not prove that the test is biased toward false positive identification.

Consider that if nobody at all has tuberculosis, the only kind of false result possible is a false positive.