To evaluate fairness in this scenario, we need to identify attributes that have a potential impact on the execution of the process and the outcomes of the cases. 

Attributes related to attributes of a case (case:citizen, case:gender, case:german speaking, case:private_insurance, case:underlying_condition) could affect the fairness of the process, as they can influence the decision of what treatment to give, and therefore can indirectly impact the outcome of the process in a way that is not neutral.

Other attributes like the resource involved or the start_timestamp and time of the case are not sensitive for fairness because they are independent of the case-specific attributes and the process outcomes. 

So the sensitive attributes for fairness in this process are:

- case:citizen (True/False)
- case:gender (True/False)
- case:german speaking (True/False)
- case:private_insurance (True/False)
- case:underlying_condition (True/False)

Therefore, if the outcomes of the process are distributed unevenly based on these attributes, and some groups of people are systematically receiving favorable or unfavorable treatment, the fairness of the process may be compromised.