Based on the information provided, the attributes that are potentially sensitive for fairness considerations are:

1. case:citizen
2. case:gender
3. case:german speaking

These attributes are sensitive because they relate to protected characteristics that could lead to unfair bias or discrimination in the loan application process if used improperly. Here's why each is concerning:

1. case:citizen: Citizenship status is a protected characteristic in many jurisdictions. Using this information to make loan decisions could lead to discrimination against non-citizens or immigrants.

2. case:gender: Gender is a widely recognized protected characteristic. Making loan decisions based on gender could result in unfair treatment of certain gender groups.

3. case:german speaking: This attribute could be seen as a proxy for national origin or ethnicity, which are protected characteristics. Using language ability as a factor in loan decisions could potentially discriminate against non-German speakers or immigrants.

These attributes should be handled with care in any decision-making process to ensure fairness and avoid potential discrimination. It's important to monitor how these factors might influence outcomes in the loan application process and take steps to mitigate any unfair bias.

Other attributes like "activity", "concept:name", "resource", and time-related fields appear to be process-related and are less likely to raise fairness concerns directly. However, it's always important to examine how these attributes interact with the sensitive ones to ensure there are no indirect discriminatory effects.