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Generative AI has the potential to transform personalization and accessibility of education. However, it raises serious concerns about accur… (see more)acy and helping students become independent critical thinkers. In this study, we designed a helpful yet fallible AI "Peer" to help students correct fundamental physics misconceptions related to Newtonian mechanic concepts. In contrast to approaches that seek near-perfect accuracy to create an authoritative AI tutor or teacher, we directly inform students that this AI can answer up to 40\% of questions incorrectly. In a randomized controlled trial with 165 students, those who engaged in targeted dialogue with the AI Peer achieved post-test scores that were, on average, 10.5 percentage points higher—with over 20 percentage points higher normalized gain—than a control group that discussed physics history. Qualitative feedback indicated that 91% of the treatment group's AI interactions were rated as helpful. Furthermore, by comparing student performance on pre- and post-test questions about the same concept, along with experts' annotations of the AI interactions, we find initial evidence suggesting the improvement in performance does not depend on the correctness of the AI. With further research, the AI Peer paradigm described here could open new possibilities for how we learn, adapt to, and grow with AI.
This paper takes a position on how anti-misinformation AI works should be developed for the online misinformation context. We observe that t… (see more)he current literature is dominated by works that produce more information for users to process and that this function faces various challenges in bringing meaningful effects to reality. We use anti-misinformation insights from other domains to suggest a redirection of the existing line of work and identify an under-explored opportunity AI can facilitate exploring.
This paper takes a position on how anti-misinformation AI works should be developed for the online misinformation context. We observe that t… (see more)he current literature is dominated by works that produce more information for users to process and that this function faces various challenges in bringing meaningful effects to reality. We use anti-misinformation insights from other domains to suggest a redirection of the existing line of work and identify an under-explored opportunity AI can facilitate exploring.
In order to combat the creation and spread of harmful content online, this paper defines and contextualizes the concept of inauthentic, soci… (see more)etal-scale manipulation by malicious actors. We review the literature on societally harmful content and how it proliferates to analyze the manipulation strategies used by such actors and the vulnerabilities they target. We also provide an overview of three case studies of extensive manipulation campaigns to emphasize the severity of the problem. We then address the role that Artificial Intelligence plays in the development and dissemination of harmful content, and how its evolution presents new threats to societal cohesion for countries across the globe. Our survey aims to increase our understanding of not just particular aspects of these threats, but also the strategies underlying their deployment, so we can effectively prepare for the evolving cybersecurity landscape.
In this work, we propose a weak supervision pipeline SWEET: Supervise Weakly for Entity Extraction to fight Trafficking for extracting perso… (see more)n names from noisy escort advertisements. Our method combines the simplicity of rule-matching (through antirules, i.e., negated rules) and the generalizability of large language models fine-tuned on benchmark, domain-specific and synthetic datasets, treating them as weak labels.
One of the major challenges in this domain is limited labeled data. SWEET addresses this by obtaining multiple weak labels through labeling functions and effectively aggregating them. SWEET outperforms the previous supervised SOTA method for this task by 9% F1 score on domain data and better generalizes to common benchmark datasets. Furthermore, we also release HTGEN, a synthetically generated dataset of escort advertisements (built using ChatGPT) to facilitate further research within the community.
2023-12-01
Findings of the Association for Computational Linguistics: EMNLP 2023 (published)
