TuringBox: An Experimental Platform for the Evaluation of AI Systems. - Ziv Epstein; Blakeley H. Payne; Judy Hanwen Shen; Casey Jisoo Hong; Bjarke Felbo; Abhimanyu Dubey; Matthew Groh; Nick Obradovich; Manuel Cebrián; Iyad Rahwan (IJCAI, 2018)

Closing the AI Knowledge Gap. - Ziv Epstein; Blakeley H. Payne; Judy Hanwen Shen; Abhimanyu Dubey; Bjarke Felbo; Matthew Groh; Nick Obradovich; Manuel Cebrián; Iyad Rahwan (CoRR, 2018)

Evaluating Generative Adversarial Networks on Explicitly Parameterized Distributions. - Shayne O'Brien; Matthew Groh; Abhimanyu Dubey (CoRR, 2018)

Towards a new social laboratory: An experimental study of search through community participation at Burning Man. - Ziv Epstein; Micah Epstein; Christian Almenar; Matthew Groh; Niccolo Pescetelli; Esteban Moro; Nick Obradovich; Manuel Cebrián; Iyad Rahwan (CoRR, 2019)

Interpolating GANs to Scaffold Autotelic Creativity. - Ziv Epstein; Océane Boulais; Skylar Gordon; Matthew Groh (CoRR, 2020)

Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology With the Fitzpatrick 17k Dataset. - Matthew Groh; Caleb Harris; Luis Soenksen; Felix Lau; Rachel Han; Aerin Kim; Arash Koochek; Omar Badri (CVPR Workshops, 2021)

Habit Formation Dynamics: Finding Factors Associated with Building Strong Mindfulness Habits. - Robert Lewis; Yuanbo Liu; Matthew Groh; Rosalind W. Picard (HCI (39), 2021)

Human detection of machine-manipulated media. - Matthew Groh; Ziv Epstein; Nick Obradovich; Manuel Cebrián; Iyad Rahwan (Commun. ACM, 2021)

Comparing Human and Machine Deepfake Detection with Affective and Holistic Processing. - Matthew Groh; Ziv Epstein; Chaz Firestone; Rosalind W. Picard (CoRR, 2021)

Social Influence Leads to the Formation of Diverse Local Trends. - Ziv Epstein; Matthew Groh; Abhimanyu Dubey; Alex Pentland (Proc. ACM Hum. Comput. Interact., 2021)

Computational Empathy Counteracts the Negative Effects of Anger on Creative Problem Solving. - Matthew Groh; Craig Ferguson; Robert Lewis; Rosalind W. Picard (ACII, 2022)

Human Detection of Political Deepfakes across Transcripts, Audio, and Video. - Matthew Groh; Aruna Sankaranarayanan; Rosalind W. Picard (CoRR, 2022)

Identifying the Context Shift between Test Benchmarks and Production Data. - Matthew Groh (CoRR, 2022)

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; Matthew Groh; Pattie Maes (CoRR, 2022)

Improving dermatology classifiers across populations using images generated by large diffusion models. - Luke W. Sagers; James A. Diao; Matthew Groh; Pranav Rajpurkar; Adewole S. Adamson; Arjun K. Manrai (CoRR, 2022)

Towards Transparency in Dermatology Image Datasets with Skin Tone Annotations by Experts, Crowds, and an Algorithm. - Matthew Groh; Caleb Harris; Roxana Daneshjou; Omar Badri; Arash Koochek (Proc. ACM Hum. Comput. Interact., 2022)

Towards Reliable Dermatology Evaluation Benchmarks. - Fabian Gröger; Simone Lionetti; Philippe Gottfrois; Álvaro González-Jiménez; Matthew Groh; Roxana Daneshjou; Labelling Consortium; Alexander A. Navarini; Marc Pouly (ML4H@NeurIPS, 2023)

SelfClean: A Self-Supervised Data Cleaning Strategy. - Fabian Gröger; Simone Lionetti; Philippe Gottfrois; Álvaro González-Jiménez; Ludovic Amruthalingam; Labelling Consortium; Matthew Groh; Alexander A. Navarini; Marc Pouly (CoRR, 2023)

Art and the science of generative AI: A deeper dive. - Ziv Epstein; Aaron Hertzmann; Laura Mariah Herman; Robert Mahari; Morgan R. Frank; Matthew Groh; Hope Schroeder; Amy Smith; Memo Akten; Jessica Fjeld; Hany Farid; Neil Leach; et al. (CoRR, 2023)

Augmenting medical image classifiers with synthetic data from latent diffusion models. - Luke W. Sagers; James A. Diao; Luke Melas-Kyriazi; Matthew Groh; Pranav Rajpurkar; Adewole S. Adamson; Veronica Rotemberg; Roxana Daneshjou; Arjun K. Manrai (CoRR, 2023)

Intrinsic Self-Supervision for Data Quality Audits. - Fabian Gröger; Simone Lionetti; Philippe Gottfrois; Álvaro González-Jiménez; Ludovic Amruthalingam; Matthew Groh; Alexander A. Navarini; Marc Pouly (NeurIPS, 2024)

How to Distinguish AI-Generated Images from Authentic Photographs. - Negar Kamali; Karyn Nakamura; Angelos Chatzimparmpas; Jessica Hullman; Matthew Groh (CoRR, 2024)

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; Ziv Epstein; Pattie Maes (CoRR, 2024)

Deepfake Detection in Super-Recognizers and Police Officers. - Meike Ramon; Matthew J. Vowels; Matthew Groh (IEEE Secur. Priv., 2024)

Deceptive Explanations by Large Language Models Lead People to Change their Beliefs About Misinformation More Often than Honest Explanations. - Valdemar Danry; Pat Pataranutaporn; Matthew Groh; Ziv Epstein (CHI, 2025)

Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images. - Negar Kamali; Karyn Nakamura; Aakriti Kumar; Angelos Chatzimparmpas; Jessica Hullman; Matthew Groh (CHI, 2025)

CleanPatrick: A Benchmark for Image Data Cleaning. - Fabian Gröger; Simone Lionetti; Philippe Gottfrois; Álvaro González-Jiménez; Ludovic Amruthalingam; Elisabeth Victoria Goessinger; Hanna Lindemann; Marie Bargiela; Marie Hofbauer; Omar Badri; Philipp Tschandl; Arash Koochek; et al. (CoRR, 2025)

When Large Language Models are Reliable for Judging Empathic Communication. - Aakriti Kumar; Nalin Poungpeth; Diyi Yang; Erina Farrell; Bruce Lambert; Matthew Groh (CoRR, 2025)