Exploring Gender Bias in Search Engines
Mots-clés :
search engines, gender bias, data diversification, algorithmic transparency, regular auditsRésumé
Search engines influence the content users access and interact with. This case study investigates the ethical
implications of gender bias in search engine algorithms through a fictional scenario involving the widely-used
search engine, "Searchandfind." The study highlights the experiences of three individuals, each encountering
biased search results that reinforce gender stereotypes. The analysis explores the technical, ethical, and societal
dimensions of these biases, emphasizing the necessity for fairness, inclusivity, and transparency in AI systems.
Practical approaches to mitigate gender bias, such as data diversification, algorithmic transparency, and regular
audits, are explored. Additionally, the study prompts reflection on the broader impact of biased AI on
professional and personal spheres, highlighting the ethical responsibility of tech companies to develop and
deploy unbiased AI systems. This examination serves as a resource for understanding and addressing the
pervasive issue of gender bias in AI-driven platforms.
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© Calvin Hillis, Ebrahim Bagheri, Zach Marshall 2024
Cette œuvre est sous licence Creative Commons Attribution 4.0 International.
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Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
