In the context of algorithmic fairness, sensitive attributes are those that may indicate membership in a protected group, which should not influence decisions or outcomes. These attributes often pertain to personal characteristics such as gender, race, age, religion, disability status, or other characteristics protected under anti-discrimination laws. In the provided event log attributes, the following can be considered sensitive attributes for fairness concerns:

1. **case:citizen** - Indicates whether the applicant is a citizen or not. This can be a sensitive attribute if citizenship status is not relevant to the outcome in the process being analyzed, such as a loan application process.

2. **case:gender** - Indicates the gender of the case. Gender should not influence the outcome in many processes and is typically considered a sensitive attribute in fairness discussions.

3. **case:german speaking** - Indicates whether the applicant speaks German. This can be a sensitive attribute, as language should not be a discriminatory factor in many processes.

For the fairness analysis in the loan application process, these attributes should not influence the outcome, such as loan approval or denial. However, if biases or discrimination exist in the process based on these attributes, it could result in unfair treatment of certain groups. For instance, if loan approval rates vary significantly based on citizenship, gender, or language, this could indicate unfairness in the process.

To assess fairness, one would analyze the correlation between these sensitive attributes and outcomes such as loan approval, as well as the performance metrics for different demographic groups. Disparities in outcome frequencies or performance metrics (e.g., higher loan denial rates for non-citizens, non-German speakers, or one gender) could indicate potential biases or discriminatory practices. Analyzing such disparities can help in identifying and mitigating bias in automated decision-making processes.