Civil Liability for Damages Caused by Artificial Intelligence in Light of Sustainable Development Goals “SDG3, 9 & 16” “A Legal Analysis Within the Saudi Civil Transactions System

Objectives: This study investigates the legal challenges and regulatory gaps in addressing civil liability for damages caused by artificial intelligence (AI) systems, with a specific focus on aligning liability frameworks with the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation and Infrastructure), and SDG 16 (Peace, Justice and Strong Institutions). It further examines the applicability of the Saudi Civil Transactions System to modern AI-related risks and proposes legal reforms to enhance justice, sustainability, and innovation. Method: A comparative legal methodology was adopted, combining doctrinal analysis of existing Saudi civil law with an international review of the EU AI Liability Directive, the U.S. tort framework, and emerging international norms. The research also includes content analysis of AI-related legal cases and simulations of AI-induced damage scenarios within civil contexts. Findings: The study reveals significant legal ambiguity regarding AI-induced damages in Saudi civil law. Only 36% of current provisions address fault-based liability applicable to autonomous systems. Furthermore, 72% of AI-related risk scenarios reviewed involve actors or systems with unclear accountability. International comparisons show that while the EU is moving toward strict liability and transparency, Saudi law still lacks provisions for “black-box” AI behavior and cross-sectoral risk management. Although AI has significant potential to contribute to SDG 3, SDG 9, and SDG 16, the current legal frameworks inadequately regulate these intersections. Novelty: This research uniquely bridges the domains of civil liability law, AI governance, and sustainable development in the Saudi legal context. It introduces a "Three-Tier Liability Framework" tailored for AI technologies, balancing preventive risk assessment with responsive redress mechanisms. The paper proposes legislative reform pathways to increase accountability coverage from the current 36% to 85% by introducing strict liability clauses for high-risk AI applications and mandatory insurance for developers and operators.