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Found 4 papers, 2 papers with code
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset
In a study, we assess if participants can identify the relevant set of attributes compared to the ground-truth.
Two4Two: Evaluating Interpretable Machine Learning - A Synthetic Dataset For Controlled Experiments
However, few of these approaches are subjected to human-subject evaluations, partly because it is challenging to design controlled experiments with natural image datasets, as they leave essential factors out of the researcher's control.
Interpretability Through Invertibility: A Deep Convolutional Network With Ideal Counterfactuals And Isosurfaces
Using PCA on the classifier’s input, we can also create “isofactuals”– image interpolations with the same outcome but visually meaningful different features.
Between Subjectivity and Imposition: Power Dynamics in Data Annotation for Computer Vision
We define data annotation as a sense-making practice, where annotators assign meaning to data through the use of labels.
