LEx: A Framework for Operationalising Layers of Machine Learning Explanations

• URL: http://arxiv.org/abs/2104.09612v1
• Date: Thu, 15 Apr 2021 23:31:04 GMT
• Title: LEx: A Framework for Operationalising Layers of Machine Learning Explanations
• Authors: Ronal Singh, Upol Ehsan, Marc Cheong, Mark O. Riedl, Tim Miller
• Abstract summary: We define a framework called the textitlayers of explanation (LEx) The framework uses the notions of textitsensitivity (emotional responsiveness) of features and the level of textitstakes (decision's consequence) in a domain to determine whether different types of explanations are textitappropriate in a given context. We demonstrate how to use the framework to assess the appropriateness of different types of explanations in different domains.
• Score: 10.46427915540552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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