Speaker
Description
Gender bias is pervasive in many aspects of our lives: women experience more difficulties than men in the job market. Here, we tested a linguistic strategy to mitigate gender bias in professional contexts in Italian.
Four versions of 20 faces were created, varying along a continuum from masculinity (1) to femininity (7) using FaceGenArtistPro3 (Values:1,3,5,7). Participants saw two versions of the same identity-face along with a word and were required to decide which face matched the word better. The experimental pairs contained the two extreme values (1 and 7) or middle values (3 and 5). In Experiment 1, professions typically associated with males (presidente, ingegnere) or females (badante, estetista) were shown. Participants more often chose the faces with feminine value (5 and 7) with professions associated with females than with those associated with males. Interestingly, faster RTs were observed when the decision confirmed the gender bias (choosing 5 and 7 for badante and estetista compared to presidente and ingegnere). This pattern suggests a gender bias in decision-making and RTs. Experiment 2 was identical except that the professions were gender-marked nouns (male bias: sindaco, sindaca; female bias: maestro, maestra). No gender bias was reported in Experiment 2, participants selected faces with feminine value (5 and 7) with sindaca and maestra, and faces with male values (1 and 3) with sindaco and maestro.
This study offers a new test to identify gender bias in decision-making and RTs. In addition, we demonstrated that by using gender-marked nouns the gender bias can be eliminated.
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