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Combining Counterfactuals With Shapley Values To Explain Image Models
With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task.
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces
Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases.
Improving Users' Mental Model with Attention-directed Counterfactual Edits
In the domain of Visual Question Answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems answer certain Image-Question (IQ) pairs.
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models
Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err.
The Impact of Explanations on AI Competency Prediction in VQA
In this paper, we evaluate the impact of explanations on the user's mental model of AI agent competency within the task of visual question answering (VQA).
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging
We suggest that this tool can improve the value of such sources in photoacoustic imaging.
A Study on Multimodal and Interactive Explanations for Visual Question Answering
Explainability and interpretability of AI models is an essential factor affecting the safety of AI.
