From fba39901f45b7c27dc5ce13f49eaa7e7a688812b Mon Sep 17 00:00:00 2001 From: ph_ Date: Sat, 3 Jun 2023 13:41:51 -0400 Subject: [PATCH] Update README.md --- README.md | 37 +++++++++++++++++++++++++------------ 1 file changed, 25 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index adb8552..9903ef7 100644 --- a/README.md +++ b/README.md @@ -121,7 +121,7 @@ Corrections or suggestions? Please file a [GitHub issue](https://github.com/jpha * **Python, R or other**: * [h2o-3](https://oreil.ly/GtGvK) -### Lecture 2 Additional Software Examples: +### Lecture 2 Additional Software Examples * [Global and Local Explanations of a Constrained Model](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_2.ipynb) * [Building from Penalized GLM to Monotonic GBM](https://nbviewer.org/github/jphall663/interpretable_machine_learning_with_python/blob/master/glm_mgbm_gbm.ipynb?flush_cache=true) * [Monotonic XGBoost models, partial dependence, individual conditional expectation plots, and Shapley explanations](https://nbviewer.org/github/jphall663/interpretable_machine_learning_with_python/blob/master/xgboost_pdp_ice.ipynb) @@ -160,27 +160,40 @@ Corrections or suggestions? Please file a [GitHub issue](https://github.com/jpha ### Lecture 3 Class Materials * [Lecture Notes](tex/lecture_3.pdf) -* Software Example: [Testing a Constrained Model for Discrimination and Remediating Discovered Discrimination](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_3.ipynb) -* [Assignment 3](https://raw.githubusercontent.com/jphall663/GWU_rml/master/assignments/tex/assignment_3.pdf) +* [Assignment 3](assignments/tex/assignment_3.pdf) +* Reading: [_Machine Learning for High-Risk Applications_](https://pages.dataiku.com/oreilly-responsible-ai), Chapter 4 and Chapter 10 -### Lecture 3 Suggested Software +### Lecture 3 Additional Software Tools -Python: +* **Python**: + * [aequitas](https://github.com/dssg/aequitas) + * [AIF360](https://github.com/IBM/AIF360) + * [Algorithmic Fairness](https://oreil.ly/JNzqk) + * [fairlearn](https://oreil.ly/jYjCi) + * [fairml](https://oreil.ly/DCkZ5) + * [solas-ai-disparity](https://oreil.ly/X9fd6) + * [tensorflow/fairness-indicators](https://oreil.ly/dHBSL) + * [Themis](https://github.com/LASER-UMASS/Themis) - * [`aequitas`](https://github.com/dssg/aequitas) - * [`AIF360`](https://github.com/IBM/AIF360) - * [`Themis`](https://github.com/LASER-UMASS/Themis) +* **R**: + * [AIF360](https://oreil.ly/J53bZ) + * [fairmodels](https://oreil.ly/nSv8B) + * [fairness](https://oreil.ly/Dequ9) -### Lecture 3 Suggested Reading +### Lecture 3 Additional Software Examples +* [Increase Fairness in Your Machine Learning Project with Disparate Impact Analysis using Python and H2O](https://nbviewer.org/github/jphall663/interpretable_machine_learning_with_python/blob/master/dia.ipynb) +* [Testing a Constrained Model for Discrimination and Remediating Discovered Discrimination](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/lecture_3.ipynb) -* **Introduction and Background**: +### Lecture 3 Additional Reading +* **Introduction and Background**: + * [*50 Years of Test (Un)fairness: Lessons for Machine Learning*](https://oreil.ly/fTlda) * **Fairness and Machine Learning** - [Introduction](https://fairmlbook.org/introduction.html) * [NIST SP1270: _Towards a Standard for Identifying and Managing Bias in Artificial Intelligence_](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf) * [*Fairness Through Awareness*](https://arxiv.org/pdf/1104.3913.pdf) - -* **Discrimination Testing and Remediation Techniques**: +* **Discrimination Testing and Remediation Techniques**: + * [*An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings*](https://oreil.ly/vmxPz) * [*Certifying and Removing Disparate Impact*](https://arxiv.org/pdf/1412.3756.pdf) * [*Data Preprocessing Techniques for Classification Without Discrimination*](https://link.springer.com/content/pdf/10.1007/s10115-011-0463-8.pdf)