Can Gender-Fair Language Reduce Gender Stereotyping and Discrimination?

By Sabine Sczesny, Magdalena Formanowicz, F. Moser. 2016

327 citations recorded by the source corpus.

Gender-fair language (GFL) aims at reducing gender stereotyping and discrimination. Two principle strategies have been employed to make languages gender-fair and to treat women and men symmetrically: neutralization and feminization. Neutralization is achieved, for example, by replacing male-masculine forms (policeman) with gender-unmarked forms (police officer), whereas feminization relies on the use of feminine forms to make female referents visible (i.e., the applicant… he or she instead of the applicant… he). By integrating research on (1) language structures, (2) language policies, and (3) individual language behavior, we provide a critical review of how GFL contributes to the reduction of gender stereotyping and discrimination. Our review provides a basis for future research and for scientifically based policy-making.

Subfields: Gender Studies, Natural Language Processing, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Gender Studies

  1. Hal Daumé Iii
  2. Hanna M. Wallach

In Natural Language Processing

  1. Hal Daumé Iii
  2. Su Lin Blodgett
  3. Hanna M. Wallach
  4. Solon Barocas
  5. David Q. Sun
  6. Hadas Kotek
  7. Rikker Dockum
  8. Yulia Tsvetkov

In Safety, Robustness & Interpretability

  1. Su Lin Blodgett
  2. Hal Daumé Iii
  3. Hanna M. Wallach
  4. Solon Barocas
  5. David Q. Sun
  6. Hadas Kotek
  7. Rikker Dockum
  8. Emilio Ferrara

Citations in the corpus