The trade-off between accuracy and bias in machine learning often leads to models being trained on a majority group, which may lead to reduced accuracy on underrepresented groups. While gathering more data and training separate models for underrepresented groups can potentially avoid this bias, it may come with additional costs for the company developing the model.