Many approaches toward characterizing the uncertainty of a prediction exist, such as mean-variance estimation, Bayesian approaches, ensembling, evidential learning, and conformal predictions, amongst many others. Most approaches tackle aleatoric uncertainty, as well as those parts of the epistemic uncertainty that are associated with the ambiguity of the model parameters. However, uncertainty from model bias is usually omitted. Even when the aleatoric error is low and plenty of data is available for training, model bias can still prove to be significant. Model bias can have many forms and causes, among them limited flexibility of the model, limited data coverage, incomplete feature representation of the input data, poor training convergence to an appropriate model, and poor generalizability of training to the test set or to actual applications. We discuss how, especially in chemical systems, uncertainty from model bias can be a large contribution toward the error in a model's prediction.