732 citations · 1.1k across the 16 of their papers we have counts for
6 papers · 1 filter
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…