Understanding AI Risks in Transportation
- Publisher: U.S. DoT, HASS COE
- Status:
final - Version:
2024 - Release Date:
2024-09-11 - Date Added:
2025-09-30 - Source URL: https://www.transportation.gov/sites/dot.gov/files/2024-09/HASS_COE_AI_Assurance_Whitepaper_AI_Risk_Sep2024.pdf
Summary
This whitepaper, part of the U.S. DOT’s AI Assurance for Transportation series, provides a foundational overview of how to identify, assess, and mitigate AI-specific risks in transportation systems, especially Highly Automated Transportation Systems (HATS). It is aimed at policymakers, system designers, safety engineers, regulators, and transportation operators who must ensure that AI components in vehicles or infrastructure operate safely, reliably, and ethically. Because AI introduces statistical and contextual uncertainties, the document is starting point for integrating AI risk management into transportation assurance frameworks.
Key Takeaways
- RIAM: Risk identification, assessment, and mitigation are the 3 core steps. The paper emphasizes applying RIAM in both design and operation time to capture and respond to hazards dynamically.
- Covers technical risks as well as system-level risks.
- Mitigations fall at the system-level and component-level; ranging from limiting AI responsibiities to runtime monitoring and safety fallback.
- Human-AI interaction is critical, and the introduction of human factors can both amplify and mitigate risk.
Additional Sources
Tags
transportation, risk-management, use-cases
License
Public-domain