Machine learning (ML) systems are rapidly increasing in size, are acquiring new
capabilities, and are increasingly deployed in high-stakes settings. As with other powerful …

The open-world deployment of Machine Learning (ML) algorithms in safety-critical
applications such as autonomous vehicles needs to address a variety of ML vulnerabilities …

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 …

Abstract Machine learning algorithms increasingly influence our decisions and interact with
us in all parts of our daily lives. Therefore, just as we consider the safety of power plants …

There is growing recognition that machine learning (ML) exposes new security and privacy
vulnerabilities in software systems, yet the technical community's understanding of the …

Machine learning has evolved into an enabling technology for a wide range of highly
successful applications. The potential for this success to continue and accelerate has placed …

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing
attention to the potential impacts of AI technologies on society. In this paper we discuss one …

Advances in machine learning (ML) in recent years have enabled a dizzying array of
applications such as data analytics, autonomous systems, and security diagnostics. ML is …

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 …