@ -191,7 +191,10 @@ In this lesson, you have learned some basics of the concepts of fairness and unf
Watch this workshop to dive deeper into the topics:
- YouTube: Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik [Fairness-related harms in AI systems: Examples, assessment, and mitigation - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k)
- Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik
[![Fairness-related harms in AI systems: Examples, assessment, and mitigation](https://img.youtube.com/vi/1RptHwfkx_k/0.jpg)](https://www.youtube.com/watch?v=1RptHwfkx_k "Fairness-related harms in AI systems: Examples, assessment, and mitigation")
> 🎥 Click the image above for a video: Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik
Also, read:
@ -199,11 +202,11 @@ Also, read:
- Microsoft’s FATE research group: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/research/theme/fate/)
Explore the Fairlearn toolkit
Explore the Fairlearn toolkit:
[Fairlearn](https://fairlearn.org/)
- [Fairlearn](https://fairlearn.org/)
Read about Azure Machine Learning's tools to ensure fairness
Read about Azure Machine Learning's tools to ensure fairness: