Impose similar metric on segments to model
Given that in your data there is correlation between population type (good vs. bad) and target, your model may learn undesirable associations between both. Therefore, the population type is a confounding factor.

A natural tool to cope with scenarios with confounders it causal inference. You can find an overview of causal inference in Judea Pearl's work, either this article or his book. A terser introduction to causal inference can be found in Ferenc Huszár's blog including an entry for controlling confounders.

There are a few python packages providing causal inference functionality, such a Microsoft's dowhy or Causalinference.