I went to a lecture today about these. Let me begin with definitions, then show how they're related.
Coverage error: The exclusion of people of people from a survey.
Algorithmic bias: Algorithmic outcomes systematically biased for a group.
Coverage error causes bias because data for eligible units not included is left out of the calculations. Coverage error can be unintentional or, as in the case of the proposed U.S. Census citizenship question, intentional. In the former case, bias estimates can often be made.
Algorithmic bias arises as a result of the same mechanism, at least in part. Facial recognition software frequently misclassifies minorities because it trained on too few minority faces. This is really a coverage error.
As an aside, I saw a demonstration in which a wolf was properly identified. When the background snow was removed, the wolf was no longer identified. The algorithm was not trained on wolves without snow.