LEx: A Framework for Operationalising Layers of Machine Learning Explanations
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Abstract (arXiv)
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 textitlayers of explanation (LEx), a lens through which we can assess the appropriateness of different types of explanations. 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 15 Apr 2021.
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15 of 28 references.
References · 15
- 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
- 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
- 2016The Mythos of Model Interpretabilitycited by 716
- 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
- 2018Manipulating and Measuring Model Interpretabilitycited by 256
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
- 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
- 2019Efficient Search for Diverse Coherent Explanationscited by 111
- 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
- 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
- 2020Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Studycited by 86
- 2019Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptionscited by 81
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