From Review to Design: Ethical Multimodal Driver Monitoring Systems for Risk Mitigation, Incident Response, and Accountability in Automated Vehicles

As vehicles approach higher levels of automation, Driver Monitoring Systems (DMS) using multimodal sensors and AI-driven inference are critical for ensuring human oversight and safety. However, these systems raise complex ethical and legal challenges around privacy, consent, data ownership, and algorithmic fairness. While existing frameworks like the GDPR, EU AI Act, and IEEE guidelines provide general direction, they lack specificity for in-cabin sensing technologies. A new paper from researchers Bilal Khana, Waseem Shariff, Rory Coyne, Muhammad Ali Farooq, and Peter Corcoran adopts a review-to-design perspective, critically examining these regulatory instruments and identifying their gaps. The authors propose a modular ethical design framework tailored specifically to DMS, translating high-level principles into actionable deployment guidance.

The framework includes user-configurable consent mechanisms, fairness-aware model development, transparency and explainability tools, and safeguards for driver emotional well-being. Additionally, the paper outlines a risk analysis and failure mitigation strategy emphasizing proactive incident response and accountability mechanisms unique to DMS. Together, these contributions aim to inform the development of transparent, trustworthy, and human-centered driver monitoring systems for next-generation autonomous vehicles. The research, submitted to arXiv under subject categories including Computers and Society and Emerging Technologies, provides a crucial resource for automakers and policymakers navigating the ethical terrain of autonomous driving.