Are Search Engines Biased? Detecting and Reducing Bias using Meta Search Engines

The search neutrality debate stems from content or service providers complaining about being discriminated and ranked unfairly low by engines, raising the need for methodologies tools to verify bias in engine rankings. For that purpose, we propose this paper a simple yet effective framework based on comparison of results provided several build corresponding tool carry out campaign tests. main objectives are develop an interpretable model behaviors design statistical tests pointing suspicious instances as possible bias, without knowing detailed ranking algorithms implemented engines. Our approach consists reasoning terms visibility engines give webpages when them among their results; different types can then be detected using outlier detection. We apply methodology test over most searched terms, which highlights some similarities discrepancies bias. interest regulators any actor Internet, is directly applicable term through publicly-available performing extensive comparisons investigations, offering two (bias-reducing) meta