732 citations · 1.1k across the 16 of their papers we have counts for
18 papers
Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation
Valdemar Danry, Pat Pataranutaporn, Matthew Groh +2
Advanced Artificial Intelligence (AI) systems, specifically large language models (LLMs), have the capability to generate not just misinformation, but also deceptive explanations t…
Augmenting medical image classifiers with synthetic data from latent diffusion models
Luke W. Sagers, James A. Diao, Luke Melas-Kyriazi +6
While hundreds of artificial intelligence (AI) algorithms are now approved or cleared by the US Food and Drugs Administration (FDA), many studies have shown inconsistent generaliza…
Art and the science of generative AI: A deeper dive
Ziv Epstein, Aaron Hertzmann, Laura Herman +11
A new class of tools, colloquially called generative AI, can produce high-quality artistic media for visual arts, concept art, music, fiction, literature, video, and animation. The…
Intrinsic Self-Supervision for Data Quality Audits
Fabian Gröger, Simone Lionetti, Philippe Gottfrois +6
Benchmark datasets in computer vision often contain off-topic images, near duplicates, and label errors, leading to inaccurate estimates of model performance. In this paper, we rev…
Improving dermatology classifiers across populations using images generated by large diffusion models
Luke W. Sagers, James A. Diao, Matthew Groh +3
Dermatological classification algorithms developed without sufficiently diverse training data may generalize poorly across populations. While intentional data collection and annota…
Deceptive AI Systems That Give Explanations Are Just as Convincing as Honest AI Systems in Human-Machine Decision Making
Valdemar Danry, Pat Pataranutaporn, Ziv Epstein +2
The ability to discern between true and false information is essential to making sound decisions. However, with the recent increase in AI-based disinformation campaigns, it has bec…
Computational Empathy Counteracts the Negative Effects of Anger on Creative Problem Solving
Matthew Groh, Craig Ferguson, Robert Lewis +1
How does empathy influence creative problem solving? We introduce a computational empathy intervention based on context-specific affective mimicry and perspective taking by a virtu…
Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm
Matthew Groh, Caleb Harris, Roxana Daneshjou +2
While artificial intelligence (AI) holds promise for supporting healthcare providers and improving the accuracy of medical diagnoses, a lack of transparency in the composition of d…
Identifying the Context Shift between Test Benchmarks and Production Data
Matthew Groh
Machine learning models are often brittle on production data despite achieving high accuracy on benchmark datasets. Benchmark datasets have traditionally served dual purposes: firs…
Human Detection of Political Speech Deepfakes across Transcripts, Audio, and Video
Matthew Groh, Aruna Sankaranarayanan, Nikhil Singh +3
Recent advances in technology for hyper-realistic visual and audio effects provoke the concern that deepfake videos of political speeches will soon be indistinguishable from authen…
Social influence leads to the formation of diverse local trends
Ziv Epstein, Matthew Groh, Abhimanyu Dubey +1
How does the visual design of digital platforms impact user behavior and the resulting environment? A body of work suggests that introducing social signals to content can increase…
Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds
Matthew Groh, Ziv Epstein, Chaz Firestone +1
The recent emergence of machine-manipulated media raises an important societal question: how can we know if a video that we watch is real or fake? In two online studies with 15,016…
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
Matthew Groh, Caleb Harris, Luis Soenksen +5
How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision…
Interpolating GANs to Scaffold Autotelic Creativity
Ziv Epstein, Océane Boulais, Skylar Gordon +1
The latent space modeled by generative adversarial networks (GANs) represents a large possibility space. By interpolating categories generated by GANs, it is possible to create nov…
Human detection of machine manipulated media
Matthew Groh, Ziv Epstein, Nick Obradovich +2
Recent advances in neural networks for content generation enable artificial intelligence (AI) models to generate high-quality media manipulations. Here we report on a randomized ex…
Towards a new social laboratory: An experimental study of search through community participation at Burning Man
Ziv Epstein, Micah Epstein, Christian Almenar +6
The "small world phenomenon," popularized by Stanley Milgram, suggests that individuals from across a social network are connected via a short path of mutual friends and can levera…
Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions
Shayne O'Brien, Matt Groh, Abhimanyu Dubey
The true distribution parameterizations of commonly used image datasets are inaccessible. Rather than designing metrics for feature spaces with unknown characteristics, we propose…
Closing the AI Knowledge Gap
Ziv Epstein, Blakeley H. Payne, Judy Hanwen Shen +6
AI researchers employ not only the scientific method, but also methodology from mathematics and engineering. However, the use of the scientific method - specifically hypothesis tes…