Profiling vs. Case-specific Evidence: A Probabilistic Analysis

Marcello Di Bello; Nicolò Cangiotti; Michele Loi

stat.OTFeb 17, 2026

Profiling vs. Case-specific Evidence: A Probabilistic Analysis

Authors: Marcello Di Bello, Nicolò Cangiotti, Michele Loi

Abstract

The use of profiling evidence in criminal trials is a longstanding controversy in legal epistemology and evidence law theory. Many scholars, even when they oppose its use at trial, still assume that profiling evidence can be probative of guilt. We reject that assumption. Profiling evidence may support a generic hypothesis, but is not evidence that the defendant is guilty of the specific crime of which they are accused. We contrast profiling evidence with case-specific evidence, which speaks more directly to the facts of the case. Our critique departs from others by grounding the argument in a probabilistic analysis of evidentiary value. We also explore the implications of our account for debates about stereotyping.

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Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence

Yutong Yao, Yanjie Cao, Guanhua Chen, Xu Yang+4

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Abductive Reasoning Legal Judgment Prediction LLM Information Extraction Demographic Bias in Language Models

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Large Language Models (LLMs) are increasingly applied to legal and criminal justice tasks, yet existing work focuses almost exclusively on post-arrest scenarios where the suspect's identity is already known, leaving the critical pre-arrest challenge of inferring suspect characteristics from incomplete evidence largely unexplored. To fill this gap, we introduce the Profiling, Investigation, and Judgment (PIJ), comprising 2,500 real homicide cases from five countries. PIJ evaluates LLMs across three tasks that span the entire criminal investigation pipeline: criminal profiling, which requires abductive reasoning to infer suspect attributes from fragmentary scene evidence, crime process reconstruction, which tests structured information extraction, and sentence prediction, which demands legal deductive reasoning. We evaluate 9 powerful LLMs and find that performance degrades systematically as tasks shift from explicit fact extraction to implicit reasoning over unknown suspect profiles. Categories requiring inferential reasoning, such as motivation and victim-offender relationships, remain the primary bottlenecks. Further analysis reveals substantial gaps between LLMs and human experts, along with pervasive biases in gender, age, and motive attribution. Our findings indicate that pre-arrest inference from incomplete evidence remains an open challenge.

Abstract

Explore similar work

Before the Arrest: Benchmarking LLMs on Criminal Profiling from Incomplete Evidence

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