Responsible Artificial Intelligence (RAI) is widely considered as one of the greatest scientific
challenges of our time and is key to increase the adoption of Artificial Intelligence (AI) …

Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Machine Learning (ML) is now used in a range of systems with results that are reported to
exceed, under certain conditions, human performance. Many of these systems, in domains …

Although AI is transforming the world, there are serious concerns about its ability to behave
and make decisions responsibly. Many ethical regulations, principles, and frameworks for …

Responsible AI has been widely considered as one of the greatest scientific challenges of
our time and the key to increase the adoption of AI. A number of AI ethics principles …

For video surveillance, pedestrian detection assists in providing baseline data for crowd
monitoring, people counting, and event detection; for smart transport system, pedestrian …

Integration of machine learning (ML) components in critical applications introduces novel
challenges for software certification and verification. New safety standards and technical …

Due to the impressive performance of deep neural networks (DNNs) for visual perception,
there is an increased demand for their use in automated systems. However, to use deep …

The latest generation of safety standards applicable to automated driving systems require
both qualitative and quantitative safety acceptance criteria to be defined and argued. At the …

The objective of this research is to present the state of the art of the safety assurance of
Artificial Intelligence (AI)-based systems and guidelines on future correlated work. For this …