STEM and teens: an algorithm bias on a social media

Grazia Cecere (), Clara Jean (), Fabrice Le Guel () and Matthieu Manant
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Grazia Cecere: IMT-BS - DEFI - Département Droit, Économie et Finances - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], RITM - Réseaux Innovation Territoires et Mondialisation - UP11 - Université Paris-Sud - Paris 11
Clara Jean: RITM - Réseaux Innovation Territoires et Mondialisation - UP11 - Université Paris-Sud - Paris 11, EPITECH
Fabrice Le Guel: RITM - Réseaux Innovation Territoires et Mondialisation - UP11 - Université Paris-Sud - Paris 11
Matthieu Manant: RITM - Réseaux Innovation Territoires et Mondialisation - UP11 - Université Paris-Sud - Paris 11

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Abstract: We study whether online platforms might reproduce offline stereotypes of girls in the STEM disciplines. The article contributes to work that aims to shed light on the possi- ble bias generated by algorithms. We run a field experiment based on launching online ad campaigns on a popular social media platform on behalf of a French computer sci- ence engineering school. We randomize the ad campaign in order to estimate whether a message aimed at prompting girls is more displayed to girls than to boys. The ad campaign targets students in high schools in the Paris area. Our results show that on average girls received 25 fewer impressions than boys, but were more likely to click on the ad if they come across it. This bias is moderated for science oriented high schools with a large majority of girls enrolled in science track. This group of girls receive more impressions compared to other girls. The treatment ad aimed at targeting more girls has a crowding-out effect, with an ad which was, overall, less shown to all.

Keywords: Algorithm bias; Discrimination; Gender-gap; STEM education (search for similar items in EconPapers)
Date: 2018-05-14
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Published in AFSE 2018 : 67th Annual meeting of the French Economic Association, May 2018, Paris, France

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02335743

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