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

1. **case:gender**: This attribute indicates the gender of the individual, which is a protected characteristic in many jurisdictions. Ensuring fairness with respect to gender is crucial to prevent bias and discrimination.
2. **case:citizen**: This attribute may be sensitive, as it could indicate an individual's nationality or immigration status. Bias against non-citizens or individuals with a specific nationality could be a concern.
3. **case:german speaking**: While language proficiency might not be an obvious sensitive attribute, it could be related to an individual's ethnicity or national origin. Therefore, it's essential to ensure that the process doesn't disadvantage non-German speaking individuals.
4. **case:married**: Marital status is a protected characteristic in some jurisdictions, and bias against unmarried or married individuals could be a concern.

These attributes may be relevant for fairness analysis, such as:

* **Demographic Disparity**: Analyze the distribution of outcomes (e.g., acceptance rates) across different demographic groups (e.g., males vs. females).
* **Predictive Rate Parity**: Evaluate the predictive accuracy of the model across different demographic groups.
* **Equal Opportunity**: Ensure that the model provides equal opportunities for individuals from different demographic groups.

Keep in mind that fairness is a context-dependent concept and may depend on the specific laws and regulations applicable to your use case. It's essential to consult with domain experts and consider the potential biases and discrimination that may arise from the use of these attributes in your process.