Social media platforms, like TikTok, Facebook, Instagram, X and many more have become testing grounds for constant marketing experiments. Due to the algorithms powered by AI and machine learning, it’s difficult to predict which content will appear in front of which users and why. According to the report, marketers lack the ability to know with certainty if one ad performs better than another, given the absence of \”random assignment\” As a result, important messages can be unintentionally excluded from certain groups, and algorithms are so precise that they can target individuals on a personal level. Despite focussing on Facebook and Google, the UBC study \”On the Persistent Mischaracterisation of Google and Facebook A/B Tests: How to Conduct and Report Online Platform Studies\” claims that all of the major social media platforms use comparable techniques. The researchers examined all known published, peer-reviewed studies of the use of A-B testing by Facebook and. Google – that is, when different consumers are shown different ads to determine which are most effective – and uncovered significant flaws.