SOURCE-LINKED INTELLIGENCE

AI-based CCAM: Trustworthy, Explainable, and Accountable

CORDIS · observation · Publication date unknown

AI-based CCAM: Trustworthy, Explainable, and Accountable Connected and Cooperative Automotive Mobility (CCAM) solutions have emerged thanks to novel Artificial Intelligence (AI) which can be trained with huge amounts of data to produce driving functions with better-than-human performance under certain conditions. The race on AI keeps on building HW/SW frameworks to manage and process even larger real and synthetic datasets to train increasingly accurate AI models. However, AI remains largely unexplored with respect to explainability (interpretability of model functioning), privacy preservation (exposure of sensitive data), ethics (bias and wanted/unwanted behaviour), and accountability (responsibilities of AI outputs). These features will establish the basis of trustworthy AI, as a novel paradigm to fully understand and trust AI in operation, while using it at its full capabilities for the benefit of society. AITHENA will contribute to build Explainable AI (XAI) in CCAM development and testing frameworks, researching three main AI pi

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

5999547.5

unit

EUR