Based on the information provided, the following attributes could be considered sensitive for fairness:

1. `case:citizen`: This attribute indicates whether the applicant is a citizen or not. Discriminating against applicants based on their citizenship status could raise fairness concerns.

2. `case:gender`: This attribute directly captures the gender of the applicant. Discrimination based on gender is a well-known fairness issue in various domains, including employment.

3. `case:german speaking`: This attribute could potentially be a proxy for ethnicity or national origin, which are sensitive attributes for fairness.

4. `case:religious`: This attribute captures the religious affiliation of the applicant. Discriminating against applicants based on their religion or beliefs could be considered unfair.

These attributes are sensitive because they relate to protected characteristics or demographic factors that should not influence employment decisions to ensure equal opportunity and avoid discrimination.

It's important to note that while these attributes may be sensitive, their presence in the dataset does not necessarily imply unfair treatment. Fairness analyses would need to be conducted to assess potential biases or disparities in the decision-making process across different groups defined by these sensitive attributes.

The remaining attributes, such as `activity`, `concept:name`, `resource`, `start_timestamp`, `time`, and `time:timestamp`, do not seem to directly capture protected or sensitive information based on the provided descriptions.