Work

SCRAM: A Scenario-Based Framework for Evaluating Regulatory and Fairness Risks in AI Surveillance Systems

Year: 2025

Type: article

Abstract: As artificial intelligence systems increasingly govern public safety operations, concerns over algorithmic fairness and legal compliance intensify. This study introduces a scenario-based evaluation framework (SCRAM) that simultaneously measures regulatory conformity and bias risks in AI-enabled surveillance. Using license plate recognition (LPR) systems in Türkiye as a case study, we simulate multiple operational configurations that vary decision thresholds and data retention periods. Each configuration is assessed through fairness metrics (SPD, DIR) and a compliance score derived from KVKK (Türkiye’s Personal Data Protection Law) and constitutional jurisprudence. Our findings show that technical performance does not guarantee normative acceptability: several configurations with high detection accuracy fail to meet legal and fairness thresholds. The SCRAM model offers a modular and adaptable approach to align AI deployments with ethical and legal standards and highlights how policy-sensitive parameters critically shape risk landscapes. We conclude with implications for real-time audit systems and cross-jurisdictional AI governance.

Source: Applied Sciences

DOI: 10.3390/app15169038

Authors: Kadir Kesgin, Selahattin Kosunalp, Ivan Beloev

Institutions: Bandırma Onyedi Eylül University, Angel Kanchev University of Ruse

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDG: Peace, justice, and strong institutions

Open Access status: gold

Estimated APC paid: $2,896