Data-Dependent Goal Modeling for ML-Enabled Law Enforcement Systems

2026-01-09

arXiv.org

AI Summary

This study addresses the challenges law enforcement agencies face in deploying machine learning systems for serious crime investigations, where data overload and limited resources are compounded by a frequent disconnect between operational objectives and data requirements. To bridge this gap, the authors propose a novel paradigm integrating Goal-Oriented Requirements Engineering (GORE) with data-driven development. Leveraging the KAOS framework, they iteratively model the bidirectional dependencies among investigative goals, data characteristics, and model performance, and introduce the first reference model that formally unifies GORE with ML development. The approach was successfully applied to a suspect identification system for online child sexual exploitation cases, demonstrating its utility in guiding requirements engineering in high-stakes contexts while also exposing limitations of KAOS in explicit data modeling—thereby offering empirical grounding for the formal application of GORE in societally critical systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Knowledge Representation and Reasoning: Knowledge Engineering
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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). This paper reports our experience applying the GORE framework KAOS to designing an ML-enabled system for identifying suspects in online child sexual abuse. We describe how KAOS supported early requirements elaboration, including goal refinement, object modeling, agent assignment, and operationalization. A key finding is the central role of data elicitation: data requirements constrain refinement choices and candidate agents while influencing how goals are linked, operationalized, and satisfied. Conversely, goal elaboration and agent assignment shape data quality expectations and collection needs. Our experience highlights the iterative, bidirectional dependencies between goals, data, and ML performance. We contribute a reference model for integrating GORE with data-driven system development, and identify gaps in KAOS, particularly the need for explicit support for data elicitation and quality management. These insights inform future extensions of KAOS and, more broadly, the application of formal GORE methods to ML-enabled systems for high-stakes societal contexts.

Problem

• Goal-Oriented Requirements Engineering
• Machine Learning
• Law Enforcement
• Data Elicitation
• KAOS

Innovation

• Goal-Oriented Requirements Engineering
• KAOS
• data elicitation
• ML-enabled systems
• requirements-data co-evolution