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

1. **case:citizen**: This attribute indicates whether a case ( likely a loan application) is from a citizen or not. This attribute can be sensitive for fairness as it may be used to discriminate against non-citizens.
2. **case:gender**: This attribute indicates the gender of the case ( likely the loan applicant). This attribute can be sensitive for fairness as it may be used to discriminate against a particular gender.
3. **case:german speaking**: This attribute indicates whether the case ( likely the loan applicant) speaks German or not. This attribute can be sensitive for fairness as it may be used to discriminate against individuals who do not speak German.

These attributes are sensitive because they relate to protected characteristics, such as nationality, gender, and language proficiency, which are often protected by anti-discrimination laws. It is essential to ensure that the process does not unfairly discriminate against individuals based on these characteristics.

In the context of fairness, it is crucial to analyze how these attributes interact with the process outcomes, such as loan approvals or rejections, to identify potential biases or discriminatory patterns.