1k citations · 1.2k across the 11 of their papers we have counts for
13 papers
Humans, AI, and Context: Understanding End-Users' Trust in a Real-World Computer Vision Application
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky +2
Trust is an important factor in people's interactions with AI systems. However, there is a lack of empirical studies examining how real end-users trust or distrust the AI system th…
UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong +1
Concept-based explanations for convolutional neural networks (CNNs) aim to explain model behavior and outputs using a pre-defined set of semantic concepts (e.g., the model recogniz…
Interactive Visual Feature Search
Devon Ulrich, Ruth Fong
Many visualization techniques have been created to explain the behavior of computer vision models, but they largely consist of static diagrams that convey limited information. Inte…
Improving Data-Efficient Fossil Segmentation via Model Editing
Indu Panigrahi, Ryan Manzuk, Adam Maloof +1
Most computer vision research focuses on datasets containing thousands of images of commonplace objects. However, many high-impact datasets, such as those in medicine and the geosc…
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky +2
Despite the proliferation of explainable AI (XAI) methods, little is understood about end-users' explainability needs and behaviors around XAI explanations. To address this gap and…
Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Ruth Fong +1
Concept-based interpretability methods aim to explain deep neural network model predictions using a predefined set of semantic concepts. These methods evaluate a trained model on a…
ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features
Vikram V. Ramaswamy, Sunnie S. Y. Kim, Nicole Meister +2
Deep learning models have achieved remarkable success in different areas of machine learning over the past decade; however, the size and complexity of these models make them diffic…
Gender Artifacts in Visual Datasets
Nicole Meister, Dora Zhao, Angelina Wang +3
Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models. Many prior works have proposed methods for mitigati…
HIVE: Evaluating the Human Interpretability of Visual Explanations
Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy +2
As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable. Despite the…
Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning
Iro Laina, Ruth C. Fong, Andrea Vedaldi
The increasing impact of black box models, and particularly of unsupervised ones, comes with an increasing interest in tools to understand and interpret them. In this paper, we con…
On Compositions of Transformations in Contrastive Self-Supervised Learning
Mandela Patrick, Yuki M. Asano, Polina Kuznetsova +4
In the image domain, excellent representations can be learned by inducing invariance to content-preserving transformations via noise contrastive learning. In this paper, we general…
Occlusions for Effective Data Augmentation in Image Classification
Ruth Fong, Andrea Vedaldi
Deep networks for visual recognition are known to leverage "easy to recognise" portions of objects such as faces and distinctive texture patterns. The lack of a holistic understand…
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth Fong, Andrea Vedaldi
As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can exp…