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Found 3 papers, 1 papers with code
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
We take inspiration from the study of human explanation to inform the design and evaluation of interpretability methods in machine learning.
BIG-bench Machine Learning
Interpretable Machine Learning
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Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory
We use an application of sensemaking in organizations as a template for discussing design guidelines for Sensible AI, AI that factors in the nuances of human cognition when trying to explain itself.
FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs
We implement our technique in a novel system, FeedLens, which is built over Semantic Scholar, a production system for navigating the scientific literature KG.
