• It would be nice to have a list of current contributors and update this list as more people add resources to this repo.

• MEPS dataset
• differential fairness metrics

In order to successfully install examples using Docker I did the following changes:

• There seems to be missing step which clones mli-resources GitHub repository. Perhaps RUN git clone https://github.com/h2oai/mli-resources.git should be added to Dockerfile (I cloned repo manually).
• Jupyter refuses to start under root - consider adding --allow-root parameter: `docker run -i -t -p 888

Hi, I noticed that some lines in the example are different from example.py, and seem to call (presumably) deprecated functions.

Regarding from fairml import plot_generic_dependence_dictionary:
There is no plot_generic_dependence_dictionary. Importing plot_dependencies worked for me.

Also,
`fig = plot_dependencies(
total.get_compress_dictionary_into_key_median(),
reverse_val

we can copy this from the documentation but would be good for people at first glance to see how it can be used (cmd line, web demo, python)

Our documentation:
https://fairlearn.readthedocs.io/en/latest/index.html
does not consistently provide links to definitions of Estimators (which should come from sklearn), numpy.ndarray and pandas. These should be remedied.

The purpose of this issue is to add support for stratified mean difference and normalized mean difference so that we can control for other explanatory (or confounding) factors that may be driving the mean difference in outcome y between the advantaged and disadvantaged groups s_+ and s_-

If different sensitive attribute values use different thresholds, the equalized odds intervention won't be sync across the values.

Therefore, an updated version of roc_curve from sklearn should be used, that takes the global thresholds and generate (fpr,tpr) for each sensitive attribute value:

https://github.com/scikit-learn/scikit-learn/blob/7b136e92acf49d46251479b75c88cba632de1937/sklearn/