STEM and teens: an algorithm bias on a social media

Grazia Cecere (), Fabrice Le Guel, Matthieu Manant and Clara Jean ()
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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], RITM - Réseaux Innovation Territoires et Mondialisation - UP11 - Université Paris-Sud - Paris 11, LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - TEM - Télécom Ecole de Management
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
Clara Jean: 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 possible 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 science 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

Date: 2017-10-27
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Published in CODE 2017 : The 4th Annual Conference on Digital Experimentation @MIT, Oct 2017, Massachussets Instutute Of Technology, Boston, United States

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02373785

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