Exploring Gender Bias in Search Engines
Calvin Hillis, Ebrahim Bagheri, Zack Marshall · The International Review of Information Ethics · 2024
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.