Unveiling Patterns in Search Autocomplete: Auditing Search Suggestions by Search Engines Across the Gender Spectrum

This study investigates the patterns present in search autocomplete suggestions generated by two widely used search engines when queried with gender-related terms: female, male, lesbian, gay, bisexual, and Transgender. By auditing the top autocomplete suggestions for each term across both platforms, the research aims to identify how gender and sexual identity are represented in algorithmically generated search prompts. A comparative analysis is conducted to examine overlaps, divergences, and potential framing differences between the two search engines. The findings highlight the need for comprehensive gender education, active efforts to challenge stereotypes, and balanced representation across sectors. Furthermore, the study emphasizes the responsibility of search engines to improve algorithmic transparency, detect and mitigate bias, and promote a more equitable information ecosystem. Addressing these challenges demands proactive, multi-dimensional efforts that extend beyond digital spaces into the fabric of society itself—through education, cultural transformation, balanced representation, and sustained research to promote a genuinely inclusive future.