IEEE Technology and Society Magazine - June 2023 - 45

Figure 2. Example output of the natural language understanding (NLU)
module for the extraction of skill from the job posting text for the
matching algorithm [9].
Clicks on " apply now " per
100 visitors
35,0
30,0
25,0
20,0
15,0
10,0
Origi
nal
Optimized
Figure 3. Results of an A/B-test comparing
the number of applications per 100
visitors (readers) of the job advertisement
web pages before and after the
reformulation of the text by removing the
strongly man-connoted words (based on
analysis of 50 advertisements and about
1,000 visitors).
June 2023
less biased toward the stereotypically manly-connoted
sentiment. It was previously assumed that men
were not affected by biased language. Our data, however,
have shown that both women and (to a lesser
degree) men are more likely to apply if the advertisement
is formulated with fewer male-connoted
words [14], [16]. This has important implications for
recruiters who want to increase their applicant pool
while enhancing its diversity [21].
In addition to the possibilities that AI applications
in recruiting open, we must emphasize the potential
new risks. The expectations of rationality and neutrality
are not necessarily fulfilled by AI decision systems
trained on historical data of human decisions.
Based on statistical learning, even the best AI models
available are not immune to learning bias from us
humans. For example, a state-of-the-art image-generating
model [22] exhibits gender bias. If asked to
produce an image of a " flight attendant, " it would
create only pictures of women [23], even though the
term " flight attendant " is gender-neutral [24]. Similarly,
when asked to produce an image of a " lawyer, "
it would only generate images of men. A similar bias
is also present in the outputs of the currently best
text-generating model [25].
45

IEEE Technology and Society Magazine - June 2023

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