STEM and Teens: An Algorithmic Bias on a Social Media

30 Pages Posted: 23 May 2018 Last revised: 14 Mar 2020

See all articles by Grazia Cecere

Grazia Cecere

Institut Mines Telecom, Business School, LITEM

Clara Jean

Epitech & Université Paris Saclay

Fabrice Le Guel

Université Paris-Sud

Matthieu Manant

Université Paris XI Sud

Date Written: February 28, 2020

Abstract

We propose a field experiment on a social media platform to investigate the sensitivity of an algorithm to different ad texts in the case of distribution of an ad related to science, technology, engineering and mathematics (STEM) education. We ran advertising campaigns on behalf of an engineering school targeting high schools in France, one with gender-unspecified ad text (control) and one with women-specific ad text (treatment). The treatment ad with the women-specific text was displayed less to all users. Although it might seem that the ad algorithm was being discriminatory, the distribution pattern is explained by the sensitivity of the algorithm to the ad text. The outcome was the result of both text length and the meaning of the word in the treatment ad especially in the case of displays to individuals aged over 18 years.

Keywords: Advertising, Algorithm bias, Field experiment

JEL Classification: M31, M37

Suggested Citation

Cecere, Grazia and Jean, Clara and Le Guel, Fabrice and Manant, Matthieu, STEM and Teens: An Algorithmic Bias on a Social Media (February 28, 2020). Available at SSRN: https://ssrn.com/abstract=3176168 or http://dx.doi.org/10.2139/ssrn.3176168

Grazia Cecere (Contact Author)

Institut Mines Telecom, Business School, LITEM ( email )

9, rue Charles Fourier
Évry, Ile de France 91011
France

Clara Jean

Epitech & Université Paris Saclay ( email )

24 rue pasteur
Le Kremlin-Bicêtre, 94270
France

Fabrice Le Guel

Université Paris-Sud ( email )

Faculté Jean-Monnet, 54, bd Desgranges
Sceaux, 92331
France

Matthieu Manant

Université Paris XI Sud ( email )

15, rue Georges Clemenceau
Orsay cedex, 91405
France

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
181
Abstract Views
1,188
rank
181,840
PlumX Metrics