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@@ -36,9 +36,10 @@ In this hands-on tutorial we will bridge the gap between research and practice,
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[Google Slides](https://docs.google.com/presentation/d/1V3FYFlLX_L8EbQ1h5VBb0YUkWNpySNPGpUPPTR05ZSY/edit?usp=sharing)
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[Interactive versions hosted on colab](https://dssg.github.io/fairness_tutorial/notebooks/)
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[Static jupyter notebooks](https://github.com/dssg/fairness_tutorial/tree/master/notebooks/)
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[Interactive versions hosted on colab](https://dssg.github.io/fairness_tutorial/notebooks/)
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1. Overall fairness and equity when building Data Science/ML systems
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* Fairness in Systems and Outcomes

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