Building explainability into the components of machine-learning models - MIT News

MIT researchers have created a taxonomy and outlined steps that developers can take to design features in machine-learning models that are easier for decision-makers to understand.

Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predi… [+6390 chars]

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