Introduction to AITHENA: AI-BASED CCAM: TRUSTWORTHY, EXPLAINABLE AND ACCOUNTABLE
Egileak: Itziar Sagastiberri Fernández
Data: 22.05.2023
Abstract
CCAM solutions have emerged thanks to novel 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 in CCAM development and testing frameworks,
researching three main AI pillars: data (real/synthetic data management), models (data fusion, hybrid AI
approaches), and testing (physical/virtual XiL set-ups with scalable MLOps).
BIB_text
title = {Introduction to AITHENA: AI-BASED CCAM: TRUSTWORTHY, EXPLAINABLE AND ACCOUNTABLE},
pages = {7},
keywds = {
Explainable AI and CCAM
}
abstract = {
CCAM solutions have emerged thanks to novel 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 in CCAM development and testing frameworks,
researching three main AI pillars: data (real/synthetic data management), models (data fusion, hybrid AI
approaches), and testing (physical/virtual XiL set-ups with scalable MLOps).
}
date = {2023-05-22},
}
