Our project investigates two key societal challenges. First, how can we design responsible AI technologies to curb the digital spread of disinformation? Second, by grounding research on responsible AI in the context of a real societal challenge – disinformation – what broader lessons can we learn about how to better design, build, and evaluate responsible AI technologies in general? For example, while there is vast technological research on creating explainable AI models, much of this research today lacks a real, practical use case and meaningful evaluation. Moreover, because disinformation often targets and disproportionately impacts different demographic groups, how can we measure and ensure the fairness of AI models for prioritizing human effort in fact checking? The problem of disinformation thus offers an invaluable, concrete testbed for grounding broader research in developing and testing fair and explainable AI models.
Larissa Doroshenko and Josephine Lukito. Trollfare: Russia’s Disinformation Campaign During Military Conflict in Ukraine. International Journal of Communication.
Sina Fazelpour, Maria De-Arteaga. “Diversity in Sociotechnical Machine Learning Systems.” Big Data and Society.
Nikhil L. Kolluri and Dhiraj Murthy. “CoVerifi: A COVID-19 News Verification System.” Online Social Networks and Media.
Chenyan Jia, Alexander Boltz, Angie Zhang, Anqing Chen, and Min Kyung Lee. Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation. ACM CSCW 2022.
Li Shi, Nilavra Bhattacharya, Anubrata Das, Matthew Lease, and Jacek Gwizdka. The Effects of Interactive AI Design on User Behavior: An Eye-tracking Study of Fact-checking COVID-19 Claims. Proceedings of the 7th ACM SIGIR Conference on Human Information, Interaction and Retrieval.