AIF360
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Trusted-AI
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
- [ ] [demo_mdss_classifier_metric.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/demo_mdss_classifier_metric.ipynb) - [ ] [demo_mdss_detector.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/demo_mdss_detector.ipynb) - [ ] [demo_meta_classifier.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/demo_meta_classifier.ipynb) - [ ] [demo_optim_data_preproc.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/demo_optim_data_preproc.ipynb)
- [x] [demo_adversarial_debiasing.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/demo_adversarial_debiasing.ipynb) - [x] [demo_calibrated_eqodds_postprocessing.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/demo_calibrated_eqodds_postprocessing.ipynb) - [x] [demo_disparate_impact_remover.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/demo_disparate_impact_remover.ipynb) - [x] [demo_exponentiated_gradient_reduction.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/demo_exponentiated_gradient_reduction.ipynb)
Get the below notebooks to work out of the box on Google colab - [ ] [demo_exponentiated_gradient_reduction_sklearn.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/sklearn/demo_exponentiated_gradient_reduction_sklearn.ipynb) - [ ] [demo_fairadapt_sklearn.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/sklearn/demo_fairadapt_sklearn.ipynb) - [ ] [demo_grid_search_reduction_classification_sklearn.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/sklearn/demo_grid_search_reduction_classification_sklearn.ipynb) - [ ] [demo_grid_search_reduction_regression_sklearn.ipynb](https://github.com/Trusted-AI/AIF360/blob/master/examples/sklearn/demo_grid_search_reduction_regression_sklearn.ipynb)
https://github.com/Trusted-AI/AIF360/blob/master/aif360/sklearn/datasets/openml_datasets.py Add a wrapper around this module so that datasets can be directly accessed using the wrapper Instead of doing something like this, ``` from sklearn.datasets import fetch_openml from aif360.sklearn.datasets.utils...
Create class / function which takes in a two lists of values for separate metrics (e.g. accuracy and disparate impact ratio) and generate + display the [Pareto frontier](https://en.wikipedia.org/wiki/Pareto_front). Guidance on...
Implement [Equalized Odds Postprocessing](https://aif360.readthedocs.io/en/stable/modules/generated/aif360.algorithms.postprocessing.EqOddsPostprocessing.html#aif360.algorithms.postprocessing.EqOddsPostprocessing) in the sklearn-compatible version of the toolkit. For reference, see how [Calibrated Equalized Odds Postprocessing](https://aif360.readthedocs.io/en/stable/modules/generated/aif360.sklearn.postprocessing.CalibratedEqualizedOdds.html#aif360.sklearn.postprocessing.CalibratedEqualizedOdds) is implemented currently in the sklearn-compat side of aif360.
- [ ] Add **kwargs to pass through to `NearestNeighbors` to configure the distance metric - [ ] ~Add option to minmax scale X before fitting KNN~ This can happen...
Refer to [here](https://github.com/Trusted-AI/AIF360/tree/master/examples/sklearn) for examples of other demo notebooks.
https://mybinder.org/
It would be nice if we could link the examples we already have to their respective API doc pages. Can we use [Sphinx-gallery](https://sphinx-gallery.github.io/stable/index.html)? Will it format notebooks properly? How automated...
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A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.