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TOWARDSDATASCIENCE

Five critical questions to explain Explainable AI

Five critical questions to explain Explainable AI

towardsdatascience.com

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Explainable AI is a critical element of the broader discipline of responsible AI. Responsible AI encompasses ethics, regulations, and governance across a range of risks and issues related to AI including bias, transparency, explicability, interpretability, robustness, safety, security, and privac...

The audience for the explanation or whom to explain should be the first question to answer. Understanding the motivation of the audience, what action or decision the audience is planning to make, their mathematical or technical knowledge and expertise are all important aspects that should be cons...

End users require an explanation of the decision or action recommended made by the AI system in order to carry out the recommendation.

Business users require an explanation to ensure corporate governance and manage reputational risk to their group or company.

Data scientists require...

Explanations may be generated before the model is built, also called ex-ante or the model is trained and tested first and then the explanation may be generated, also called post-hoc.

Visual or graphical explanations, tabular data-driven explanation, natural language descriptions or voice explanations are some of the existing modes of explanation.

A salesperson might be comfortable with an explanation that shows a graph of increasing sales and how the increase in sales i...

There are six broad approaches as it relates to post-hoc explainability.

  • Feature relevance: These approaches to explainability focus on the inner functioning of the model and highlight the features that best explain the outcome of the model.
  • Model simplification: These approach...

There are six broad approaches as it relates to post-hoc explainability.

  • Feature relevance: These approaches to explainability focus on the inner functioning of the model and highlight the features that best explain the outcome of the model.
  • Model simplification: These approach...

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