AI Ethics and Legal Accountability in Engineering Decision Support Systems
Sellamuthu Palanisamy Vijayanand, Prashant Sangulagi, Dhananjay S. Pawar · 2025
The integration of Artificial Intelligence (AI) into engineering decision support systems (EDSS) has transformed traditional engineering practices by enabling data-driven, predictive, and autonomous decision-making. While AI enhances efficiency, accuracy, and innovation in complex engineering domains, it simultaneously raises critical ethical and legal challenges. This chapter examines the intersection of AI ethics and legal accountability in engineering, emphasizing the need for transparent, fair, and explainable systems that uphold public safety and professional integrity. Key ethical considerations addressed include bias mitigation, human oversight, safety, and data privacy, while legal accountability explores liability attribution, intellectual property, regulatory compliance, and organizational responsibility in multi-stakeholder AI ecosystems. The discussion further identifies gaps in existing regulatory frameworks, highlighting the challenges of harmonizing international liability laws and bridging the divide between ethical principles and legal enforceability. Governance strategies are proposed to integrate risk management, adaptive compliance mechanisms, and collaborative ownership models, ensuring that AI-driven engineering systems operate within robust ethical and legal frameworks. Through sectoral case studies and analysis of emerging standards, the chapter provides actionable insights into designing AI systems that balance innovation with societal trust, professional accountability, and regulatory adherence. The findings underscore the imperative of embedding ethical and legal considerations from the design phase through deployment, establishing a foundation for responsible, transparent, and legally compliant AI in engineering decision support.