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arXiv · 2104.09612

LEx: A Framework for Operationalising Layers of Machine Learning Explanations

Ronal Singh·Upol Ehsan·Marc Cheong·Mark O. Riedl·Tim Miller
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Abstract

Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the \textit{layers of explanation} (LEx), a lens through which we can assess the appropriateness of different types of explanations. The framework uses the notions of \textit{sensitivity} (emotional responsiveness) of features and the level of \textit{stakes} (decision's consequence) in a domain to determine whether different types of explanations are \textit{appropriate} in a given context. We demonstrate how to use the framework to assess the appropriateness of different types of explanations in different domains.

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Ronal Singh, Upol Ehsan, Marc Cheong, Mark O. Riedl, Tim Miller. 2021-04-15. LEx: A Framework for Operationalising Layers of Machine Learning Explanations. https://arxiv.org/abs/2104.09612

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