LEx: A Framework for Operationalising Layers of Machine Learning Explanations
This work addresses the challenge of tailoring AI explanations to social factors for users affected by AI decisions, but it is incremental as it builds on existing explanation methods without introducing new technical solutions.
The paper tackles the problem of selecting appropriate AI explanations for users by proposing the LEx framework, which uses feature sensitivity and decision stakes to evaluate explanation suitability across different contexts.
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.