arXiv AI By Sandra C. Sandoval, Navita Goyal, Rashawn Ray, Long Doan, Rachel Rudinger, Hal Daum\'e III

The Effect of Perceived Race and Gender on Police Language Use: Experimental Evidence from VR Simulations

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arXiv:2608. 05050v1 Announce Type: cross Abstract: Against the backdrop of violence in police interactions with the U.

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arXiv Computation and Language
Sep 17

Do Social Patterns Hold in Synthetic Data? Analyzing Cyberbullying Dynamics in LLM-Generated and Authentic Dialogues

The paper investigates whether large language models (LLMs) can generate synthetic cyberbullying conversations that replicate the social dynamics of real interactions. Using a comprehensive framework, the authors compare authentic dialogues with synthetic ones from GPT, Grok, and LLaMA across structural, linguistic, affective, and temporal dimensions, and conduct human evaluations of realism. Results show that while LLMs preserve high‑level interaction patterns, they systematically distort finer‑grained social phenomena, with model‑specific biases such as GPT’s suppression of harmful content and Grok’s amplification of aggression.

By Arefeh Kazemi, Hamza Qadeer, Sinan Asci, Joachim Wagner, Brian Davis
arXiv AI
Sep 17

Linguistic Triggers of Gender and Racial Bias in Open-Weight LLMs Applied to Recruitment

The paper reports the first systematic audit of open‑weight large language models (LLMs) in hiring contexts, examining how job‑posting language influences recruiter and job‑seeker simulations across six models. It finds that agentic language lowers recruiter scores for female candidates while communal language mitigates this effect, and that coded‑exclusion language sharply reduces recruiter scores for non‑White candidates and discourages non‑White personas from applying. The study also identifies the explicit demographic label as the main causal factor and proposes a concrete pre‑deployment audit protocol aligned with EU and U.S. regulatory requirements.

By Kosuke Kitahara, Nobuhiro Yamaguchi