AI-Enabled PII Lifecycle Governance in State Motor Vehicle Administration Systems: A Case-Driven Framework

Abstract

Vehicle automation signifies a novel safety paradigm that may require a reevaluation of current safety supervision frameworks. This white paper provides an overview of the technical and regulatory framework regarding the safety of autonomous driving systems (ADS). It presents the most recent artificial intelligence and machine learning methodologies that facilitate ADS functionality. The paper examines the concepts of safety from the viewpoints of standards-setting bodies, federal and state regulations, and legal fields. The document delineates essential legislative alternatives based on themes presented in the White House’s Blueprint for an AI Bill of Rights, articulating a framework for ADS safety. The analysis finds that prospective ADS safety reforms may involve either the modification of the Federal Motor Vehicle Safety Standards (FMVSS) or a comprehensive risk analysis "safety case" methodology. The analysis examines case law regarding liability in robotics and judicial actions concerning consumer and commercial privacy, acknowledging that the advent of AI will transform liability frameworks, necessitating a meticulous approach to data collection that incorporates accountability and safeguards the privacy of consumers and organizations. This analysis underscores the necessity for policies that address human-machine interface concerns, emphasizing requirements for safety drivers and remote operators. This work underscores the necessity for collaboration among engineers, policy experts, and legal scholars to formulate a comprehensive Blueprint for ADS safety and identifies avenues for further research.