In the context of fairness, sensitive attributes are those that could potentially lead to biased or unfair treatment if used in decision-making processes. These attributes often relate to demographic characteristics that are protected by law or social norms. Based on the provided attributes, the following could be considered sensitive for fairness:

1. **case:citizen**: This attribute indicates whether an individual is a citizen. Using this information in decision-making could lead to discrimination based on citizenship status.

2. **case:gender**: This attribute indicates the gender of the individual. Gender is a protected characteristic, and using it in decision-making could result in gender-based discrimination.

3. **case:german speaking**: This attribute indicates whether an individual speaks German. Language proficiency can sometimes be a proxy for national origin or ethnicity, which are protected characteristics. Using this attribute could lead to linguistic discrimination.

These attributes are sensitive because they relate to characteristics that are often protected by anti-discrimination laws and policies. Decision-making processes should be designed to ensure that individuals are not unfairly treated based on these attributes.

Other attributes, such as **activity**, **concept:name**, **resource**, **start_timestamp**, **time**, and **time:timestamp**, are generally not considered sensitive for fairness as they do not relate to protected characteristics. However, it's important to note that even seemingly non-sensitive attributes can sometimes lead to unfairness if they are correlated with sensitive attributes or if they are used in a way that disproportionately impacts certain groups.

In summary, the sensitive attributes for fairness in the given data are **case:citizen**, **case:gender**, and **case:german speaking**.