一句话：提出数据依赖的目标建模，以在开发早期对齐ML赋能执法系统行为与利益相关者目标。
严重犯罪调查复杂且资源受限，执法机构面临海量罪犯与事件数据，嫌疑人识别困难。尽管已有ML赋能系统尝试支持执法，但多项系统在实践中失败，说明需在开发早期使系统行为与利益相关者目标一致。本文由此提出面向ML赋能执法系统的数据依赖目标建模，借助目标导向需求工程（GORE）来刻画数据依赖关系并支撑早期目标对齐。摘要未给出具体方法细节与评估结果，主要贡献在于问题定位和建模方向。
Investigating serious crimes is inherently complex and resource-constrained. Law enforcement agencies (LEAs) grapple with overwhelming volumes of offender and incident data, making effective suspect identification difficult. Although machine learning (ML)-enabled systems have been explored to support LEAs, several have failed in practice. This highlights the need to align system behavior with stakeholder goals early in development, motivating the use of Goal-Oriented Requirements Engineering (GORE).
@inproceedings{paperbot12010,
title = {Data-Dependent Goal modeling for ML-Enabled Law Enforcement Systems},
author = {Dalal Alrajeh and Vesna Nowack and Patrick Benjamin and Katie Thomas and William Hobson and Carolina Gutierrez Muñoz and Catherine Hamilton-Giachritsis and Juliane Kloess and Jessica Woodhams and Dan Butler and Mark Law and Ralph Morton and Benjamin Costello and Amy Burrell and Tim Grant and Prachiben Shah and Frances Laureano de Leon and Mark Lee},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3786581.3786944}
}