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 ### Lecture 3 Class Materials
 
 * [Lecture Notes](tex/lecture_3.pdf)
+* [Software Example]()
 * [Assignment 3](assignments/tex/assignment_3.pdf)
   * [Model evaluation notebook](https://nbviewer.jupyter.org/github/jphall663/GWU_rml/blob/master/assignments/eval.ipynb?flush_cache=true)
   * [Full evaluations results](assignments/model_eval_2023_06_28_21_00_17.csv)
-* Reading: [_Machine Learning for High-Risk Applications_](https://pages.dataiku.com/oreilly-responsible-ai), Chapter 4 and Chapter 10
-
-### Lecture 3 Additional Software Tools
-
-* **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)
-
-* **R**:
-  * [AIF360](https://oreil.ly/J53bZ)
-  * [fairmodels](https://oreil.ly/nSv8B)
-  * [fairness](https://oreil.ly/Dequ9)
-
-### 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)
-* _Machine Learning for High-risk Applications_: [Use Cases](https://oreil.ly/machine-learning-high-risk-apps-code) (Chapter 10)
-
-### 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**:
-  * [*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)  
-  * [*Decision Theory for Discrimination-aware Classification*](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.722.3030&rep=rep1&type=pdf)
-  * [*Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification Without Disparate Mistreatment*](https://arxiv.org/pdf/1610.08452.pdf)
-  * [*Learning Fair Representations*](http://proceedings.mlr.press/v28/zemel13.pdf)
-  * [*Mitigating Unwanted Biases with Adversarial Learning*](https://dl.acm.org/doi/pdf/10.1145/3278721.3278779)
+* Reading [_Machine Learning for High-Risk Applications_](https://www.oreilly.com/library/view/machine-learning-for/9781098102425/), Chapter 4 and Chapter 10
 
 ***