About the bar at right side (Fair models)

[Nehabtkk]

Hi Jakub, I hope you are well.

I am using the fair models library for classification purposes on my data and have the following graph.. Can you interpret it, I mean what does the bar at the right side of score 1.0 mean? I know that if the bar enters the pink space, we can say that there is some bias exist but what about the bars at the right side?

Also, since I am using this library after a long time (one year probably), are there any advancements in the library? Can we now use the fairness for the output feature (earlier it was only intended for input features).

The graph is shown here.

Hi Jakub, I hope you are well.

I am using the fair models library for classification purposes on my data and have the following graph.. Can you interpret it, I mean what does the bar at the right side of score 1.0 mean? I know that if the bar enters the pink space, we can say that there is some bias exist but what about the bars at the right side?

Also, since I am using this library after a long time (one year probably), are there any advancements in the library? Can we now use the fairness for the output feature (earlier it was only intended for input features).

The graph is shown here.

[jakwisn]

Hi Neha,
Thanks for the question! I will allow myself to copy-paste a section from a tutorial: https://modeloriented.github.io/fairmodels/articles/Basic_tutorial.html

If bars reach red field on the left it means that there is bias towards certain unprivileged subgroup. If they reach one on the right it means bias towards privileged

so in other words bar starts at 1 because it is ideal ratio between metric for the unprivileged and privileged. If you divide smaller metric by higher metric the bar will be on the left, otherwise on the right.

As I think previously mentioned you can input an output variable to the package as long as it makes sense with the interpretation. But to be honest it is hard for me to come up with a good example on why to do that, so unless you are 100% sure I would not recommend it - fairness can be looked at from the prism of causality - we assume that something had an effect on something else and our model should reflect that.

[jakwisn]

Closing for now, if not clear enough please reopen