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05/15/2023
Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application
Trust is an important factor in people's interactions with AI systems. H...
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03/27/2023
UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs
Concept-based explanations for convolutional neural networks (CNNs) aim ...
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10/02/2022
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Despite the proliferation of explainable AI (XAI) methods, little is und...
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07/20/2022
Overlooked factors in concept-based explanations: Dataset choice, concept salience, and human capability
Concept-based interpretability methods aim to explain deep neural networ...
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06/15/2022
ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features
Deep learning models have achieved remarkable success in different areas...
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12/06/2021
HIVE: Evaluating the Human Interpretability of Visual Explanations
As machine learning is increasingly applied to high-impact, high-risk do...
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06/01/2021
Cleaning and Structuring the Label Space of the iMet Collection 2020
The iMet 2020 dataset is a valuable resource in the space of fine-graine...
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04/28/2021
[Re] Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Singh et al. (2020) point out the dangers of contextual bias in visual r...
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12/14/2020
Information-Theoretic Segmentation by Inpainting Error Maximization
We study image segmentation from an information-theoretic perspective, p...
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12/02/2020
Fair Attribute Classification through Latent Space De-biasing
Fairness in visual recognition is becoming a prominent and critical topi...
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03/24/2020
