Epstein, Hertzmann, et al. "Art and the science of generative AI." Science 380.6650 (2023): 1110-1111.
Epstein, Ziv. The dynamics of attention in digital ecosystems. Diss. Massachusetts Institute of Technology, 2019.
Epstein, Z., Sirlin, N., Arechar, A. A., Pennycook, G., & Rand, D. G. (2023). The Social Media Context Interferes with Truth Discernment. Science Advances.
Smith, A., Schroeder, H., Epstein, Z., Cook, M., Colton, S., & Lippman, A. (2023). Trash to Treasure: Using text-to-image models to inform the design of physical artefacts.
Epstein Z and Hause L. Yourfeed: Towards open science and interoperable systems for social media. arXiv.
Danry, Valdemar, Pat Pataranutaporn, Ziv Epstein, Matthew Groh, and Pattie Maes (Under review). “Deceptive AI systems that give explanations are just as convincing as honest AI systems in human-machine decision making.” Extended Abstract. Presented at the International Conference on Computational Social Science (IC2S2) 2022.
Matthew Groh, Ziv Epstein, Chaz Firestone, Rosalind Picard. "Deepfake detection by human crowds, machines, and machine-informed crowds." Proceedings of the National Academy of Sciences Jan 2022, 119 (1) e2110013119; DOI: 10.1073/pnas.2110013119
Epstein, Ziv, et al. "Social influence leads to the formation of diverse local trends." Proceedings of the ACM on Human-Computer Interaction 5.CSCW2 (2021): 1-18.
Pennycook, G., Epstein, Z., Mosleh, M. et al. Shifting attention to accuracy can reduce misinformation online. Nature (2021). https://doi.org/10.1038/s41586-021-03344-2
Epstein, Ziv, et al. "Who gets credit for AI-generated art?." iScience (2020): 101515.
Epstein, Ziv, Gordon Pennycook, and David Rand. "Will the crowd game the algorithm? Using layperson judgments to combat misinformation on social media by downranking distrusted sources." (2019).
Devadoss, Satyan, Ziv Epstein, and Dmitriy Smirnov. "Visualizing Scissors Congruence." LIPIcs-Leibniz International Proceedings in Informatics. Vol. 51. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2016.