Machine Learning Interpretability Toolkit | AI Show

Posted on Posted in AI, Artificial Intelligence, Machine Learning

Understanding what your AI models are doing is super important both from a functional as well as ethical aspects. In this episode we will discuss what it means to develop AI in a transparent way. Mehrnoosh introduces an awesome interpretability toolkit which enables you to use different state-of-the-art interpretability methods to explain your models decisions. By using this toolkit during the training phase of the AI development cycle, you can use the interpretability output of a model to verify hypotheses and build trust with stakeholders. You can also use the insights for debugging, validating model behavior, and to check for bias. The toolkit can even be used at inference time to explain the predictions of a deployed model to the end users. 

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Segments of the video:

  • [02:12] – Responsible AI
  • [02:34] – Machine Learning Interpretability
  • [03:12] – Interpretability Use Cases
  • [05:20] – Different Interpretability Techniques
  • [06:45] – Demo

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Machine Learning Interpretability Toolkit | AI Show
Source: MSDN Channel 9