I'm unable to see the data that you've provided, so I cannot provide a specific explanation. However, in general, attribute sensitivity can be considered fair when it is not obvious if one group will be more or less likely than others to be biased.

For example, if you have a dataset with data on tenant satisfaction and income, then you may need to carefully consider which attributes are most sensitive. Attributes that relate directly to the outcomes you're interested in (e.g., whether tenant satisfaction is higher or lower) should tend to be more sensitive because they impact the outcome at a high level.

On the other hand, some attributes might not be as sensitive or relevant for fairness (such as demographics like gender or race). These might reflect characteristics that are less important to the outcomes you're interested in but do play an important role. To ensure fairness, it's often best to carefully consider which attributes need to be included and which ones can be safely dropped if they don't seem relevant.

It's also worth noting that what counts as "fairness" can vary depending on your specific research question or the context in which the data was collected. It may be helpful for you to review the guidelines for fairness in your field, such as those provided by journals and other professional organizations, to understand how to address potential biases when using datasets for analysis.