The pace at which AI theory is being delivered to application has accelerated in the last decade, creating some impressive value in some areas (e.g., health and legal informatics, manufacturing, supply chain management), but raising warning flags about trust and ethics.

Both the promise and the challenges are evident in the application of AI to automotives and automated autonomous systems, especially in the choice of technologies, tradeoffs in where intelligence is required (i.e., the autonomous system or the infrastructure), and the emerging role of explainable AI, both in improving the transparency, trust, and robustness of systems, and in informing social systems and regulators about how to confirm their safety.

We try to highlight salient aspects of these challenges, and provide some context for helping to manage the translation of AI theory to application.