In the context of the given event log and directly-follows graph, the attributes that could be considered sensitive for fairness are:

- case:citizen: This attribute indicates whether a case involves a citizen or not. This could be sensitive for fairness as there may be biases or discrimination based on citizenship status within the process.

- case:gender: This attribute indicates the gender of the individual involved in the case. Gender bias is a well-documented issue in various contexts and could impact the fairness of the process.

- case:german speaking: This attribute indicates whether the individual involved in the case speaks German or not. Language proficiency can be a sensitive attribute for fairness as it might affect the level of care or service received.

- case:private_insurance: This attribute indicates whether the individual involved has private insurance. Access to private insurance can impact the quality of healthcare services received, leading to potential fairness concerns.

- case:underlying_condition: This attribute indicates whether the individual has an underlying medical condition. Discrimination or biases based on health status can impact the fairness of the process.

It is important to pay attention to these sensitive attributes to ensure fairness, mitigate biases, and promote equity in the process described in the event log.