Backend
Sure Enough to Share? Overconfidence in GenAI Video Discernment is Associated with Sharing Intentions
arXiv cs.HC0 views
arXiv:2611.05844v1 Announce Type: new Abstract: Generative Artificial Intelligence (GenAI) is blurring the line between synthesized and recorded videos, but less is known about people's awareness of their discernment abilities and the consequences of miscalibration. This study examines three forms of overconfidence in GenAI video discernment (i.e., overestimation, overplacement, and overprecision) and their associations with source judgments and sharing intentions in an online study (N = 973). Participants evaluated videos depicting everyday scenes and achieved 62.5% accuracy. Three forms of overconfidence showed distinct patterns: participants overplaced themselves by 12.8 percentile points, but overprecision showed consistent positive associations with sharing intentions and the tendency to classify videos as human-made. Age, GenAI usage, and AI literacy showed consistent positive associations with all three forms of overconfidence. These findings highlight the importance of distinguishing the three forms of overconfidence to better understand GenAI engagement and incorporating metacognition into interventions to support informed media consumption.
Source:https://arxiv.org/abs/2611.05844PreviousPairing Expert Critique with LLM-Driven Refinement: An Iterative Approach for Curating Quality AI Evaluation DatasetsNextCompiling, Validating, and Using Human Reference Atlas Cell Type Annotation Crosswalks
Related posts
Constrained Black-Box Attacks Against Cooperative Multi-Agent Reinforcement Learning
0arXiv cs.MA
Same Method, Different Task: What the Bitter Lesson Does Not License in Social Simulation
0arXiv cs.MA
Conjugate priors for Bayesian Variable Selection in exponential family Generalized Linear Models
0arXiv stat.TH
Optimal distribution-free sequential change detection with conformal martingales
0arXiv stat.TH
Uniform Central Limit Theorems for Degree Distributions of Sparse Graphon Models
0arXiv stat.TH
TAGR: Temporally Adaptive Generative Recommendation for Industrial Live-Streaming Advertising
0arXiv cs.IR