Biases in supervised learning models could limit the audience a brand is able to reach.
Supervised and unsupervised are two approaches under machine learning, which when understood well, could make a huge difference to a brand’s ad campaigns and bottom line. Misunderstanding these approaches could result in unintended cultural and racial biases.
Brands often start campaigns with a pre-conceived outcome, which results in biases being introduced. Supervised learning uses models that are generated to achieve this outcome. Contrarily, unsupervised machine learning has an undefined outcome with a learning algorithm deployed “to find patterns and structures in raw data”.
This reveals clusters of audiences that marketers were oblivious to at first. Relying on supervised learning could cause advertisers to miss out on relevant audiences. Using unsupervised learning can not only eliminate biases, but also financially benefit brands.
[3 minute read]