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Artificial intelligence and algorithmic bias? Field tests on social network with teens

Author

Listed:
  • Grazia Cecere

    (IMT-BS - DEFI - Département Droit, Economie 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 - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris])

  • Clara Jean

    (EESC-GEM Grenoble Ecole de Management)

  • Fabrice Le Guel

    (RITM - Réseaux Innovation Territoires et Mondialisation - Université Paris-Saclay)

  • Matthieu Manant

    (RITM - Réseaux Innovation Territoires et Mondialisation - Université Paris-Saclay)

Abstract

Artificial intelligence (AI) is a general purpose technology that is used in many sectors. However, automated decision-making powered by AI algorithms can lead to unintended outcomes, especially in the context of online platforms. The lack of transparency related to AI algorithms and their categorization methods make practical insights into effective management of the risks associated to their utilization of crucial importance. We address these issues through two field tests aimed at mitigating biases in online science, technology, engineering, and mathematics (STEM) education-related ads targeting teenagers. We conducted online ad campaigns involving gender-unspecific, women-specific, and gender-neutral ads targeted at young social network users. Our findings show that inclusion in the ad of a gender-oriented message tends to alleviate algorithmic gender bias but also reduced overall ad visibility. Our research shows also that text length has a significant impact on ad visibility, and that gender-oriented messages influence the display of the ad based on gender.

Suggested Citation

  • Grazia Cecere & Clara Jean & Fabrice Le Guel & Matthieu Manant, 2024. "Artificial intelligence and algorithmic bias? Field tests on social network with teens," Post-Print hal-04464964, HAL.
    • Handle: RePEc:hal:journl:hal-04464964
    DOI: 10.1016/j.techfore.2023.123204
    as

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