Course Summary

1. Course Summary

You've completed the course - Responsible AI for Developers: Interpretability and Transparency. Let's recap what you have learned. In this course, we introduced interpretability and transparency, which are key to mitigating unfair biases in AI. You also learned about interpretability techniques and how they are categorized into Feature-based, Concept-based, and Example-based methods. You learned about feature-based explanations where there are global techniques such as permutation, feature importance and partial dependence plots. And you learned about local methods such as LIME, Shapley values, Integrated gradients, and XRAI. You also gained knowledge around concept-based explanations, such as TCAV, which aims to provide explanations for arbitrary concepts. And you learned about example-based explanations which provide approximate nearest neighbor-based explanations. Lastly, you explored a few interpretability tools, such as open-source library SHAP, Learning Interpretability Tool, and Vertex Explainable AI, as well as a few transparency tools, such as data cards for data transparency, and model cards for model transparency. As artificial intelligence continues its rapid ascent, the conversation around responsible AI becomes ever more vital. New technological developments constantly present fresh challenges and opportunities in this domain. It's even more important now to ensure that when you develop your AI, you are equipped with the latest insights and best practices for responsible AI implementation.

2. Let's practice!

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