Hi Josh.
I think this would be cool to add at some point, I'm not sure this is now.
I'm a bit surprised by their "fairness report". They have 4 different
metrics of fairness which are conflicting.
If they are all included in the fairness report then you always fail the
fairness report, right?
I think it would also be great to provide a tool to change predictions
to be fair according to one of these
criteria.
I don't think there is consensus yet that these metrics are "good", in
particular since they are conflicting,
and so people are trying to go beyond these, I think.
Cheers,
Andy
On 10/29/18 1:36 AM, Feldman, Joshua wrote:
Hi,
I was wondering if there's any interest in adding fairness metrics to
sklearn. Specifically, I was thinking of implementing the metrics
described here:
https://dsapp.uchicago.edu/projects/aequitas/
I recognize that these metrics are extremely simple to calculate, but
given that sklearn is the standard machine learning package in python,
I think it would be very powerful to explicitly include algorithmic
fairness - it would make these methods more accessible and, as a
matter of principle, demonstrate that ethics is part of ML and not an
afterthought. I would love to hear the groups' thoughts and if there's
interest in such a feature.
Thanks!
Josh
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
