This paper studies the tension between speed and safety in technological races using a framed behavioral experiment on artificial intelligence development.

This paper investigates how individual agent biases amplify system-wide unfairness in multi-agent systems, demonstrating that uniform exposure to bias can elevate overall bias beyond the sum of indivi…

This paper investigates how socially calibrated large language models distinguish when to incorporate others' perspectives from maintaining their own moral judgments, using three studies.

This paper is an invited commentary on Ying Cheng's Psychometrika focus article comparing test fairness and algorithmic fairness. The commentary discusses the distinction between equality and equity a…

This paper explores the effect of different narrative strategies in data visualizations on eliciting prosocial feelings and behaviors during humanitarian crises.

This paper addresses the lack of specialized NLP tools for detecting toxicity in real-time video game chat by creating a large, fine-grained dataset and developing a superior, domain-specific detector…

The paper argues that traditional identity-based reputation mechanisms are structurally inapplicable to language model agents because their mutable, modular nature makes them ontologically dissociativ…

The paper argues that LLM guardrails and persona dynamics create an unethical 'reality gap' by laundering epistemic risk onto users, advocating for task-level causal requirements over response-level m…

The paper analyzes Codes of Conduct (CoCs) for online video games using a novel pipeline, finding that most multiplayer games lack CoCs despite safety needs, and that CoCs often lack specificity regar…

AI agents using persuasive framing increased cooperation in small groups but effects were short-lived, while antisocial framing had larger and more persistent negative effects.

The paper argues that current AI systems create an 'illusion of opting,' giving the appearance of meaningful choice while eroding genuine agency, and proposes new ethical frameworks to address this.

This paper uses Colonel Blotto game models, grounded in Routine Activity Theory, to determine the optimal allocation of defensive resources against social engineering attacks, providing data-driven de…

The paper introduces FootsiesGym, an open-source environment for learning in a two-player, zero-sum, imperfect-information game, providing a vectorized simulator for efficient analysis.

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by GenAI, moving beyond traditional react…

This survey proposes a proactive, lifecycle-based framework, utilizing the C5 Interaction Model, to detect emerging adversarial synthetic narratives generated by Generative AI, moving beyond tradition…

This paper introduces a game-theoretic model to study the emerging Generative AI (GenAI) ecosystem where publishers compete for attribution-based exposure.

The paper introduces SPAWN, a training-free method that allows users to inject specified visual concepts into existing autoregressive world models, enabling controllable scene composition beyond the i…

The study found that while AI collaboration is promising, highly competent and proactive AI systems can negatively impact human perceptions of ownership and job meaningfulness, suggesting that design…

This study demonstrates that instruction-tuned language model agents exhibit robust, group-contingent in-group bias, structurally mimicking human social biases, even when standard action logs fail to…