Narrative over Numbers: The Identifiable Victim Effect and its Amplification Under Alignment and Reasoning in Large Language Models

Authors: Syed Rifat Raiyan.
Abstract: The Identifiable Victim Effect (IVE)–the tendency to allocate greater resources to a specific, narratively described victim than to a statistically characterized group facing equivalent hardship–is one of the most robust findings in moral psychology and behavioral economics. As large language models (LLMs) assume consequential roles in humanitarian triage, automated grant evaluation, and content moderation, a critical question arises: do these systems inherit the affective irrationalities present in human moral reasoning? We present the first systematic, large-scale empirical investigation of the IVE in LLMs, comprising $N=51{,}955$ validated API trials across 16 frontier models spanning nine organizational lineages. Using a suite of ten experiments–porting and extending canonical paradigms from Small et al. (2007) and Kogut and Ritov (2005)–we find that the IVE is prevalent but strongly modulated by alignment training. Instruction-tuned models exhibit extreme IVE ($d=1.56$), while reasoning-specialized models invert the effect (down to $d=-0.85$). The pooled effect ($d=0.223$, $p=2 \times 10^{-6}$) is approximately twice the single-victim human meta-analytic baseline ($d \approx 0.10$) reported by Lee and Feeley (2016)–and likely exceeds the overall human pooled effect by a larger margin, given that the group-victim human effect is near zero. Standard Chain-of-Thought (CoT) prompting–contrary to its role as a deliberative corrective–nearly triples the IVE effect size (from $d=0.15$ to $d=0.41$), while only utilitarian CoT reliably eliminates it. We further document psychophysical numbing, perfect quantity neglect, and marginal in-group/out-group cultural bias, with implications for AI deployment in humanitarian and ethical decision-making contexts. Our code and data are publicly available at this https URL.