Deploying machine learning algorithms in safety critical systems raises new challenges for
system designers. The opaque nature of some algorithms together with the potentially large …

At BIML, we are interested in “building security in” to machine learning (ML) systems from a
security engineering perspective. This means understanding how ML systems are designed …

The unprecedented advancement of artificial intelligence (AI) in recent years has altered our
perspectives on software engineering and systems engineering as a whole. Nowadays …

With the increasing use of Machine Learning (ML) in critical autonomous systems, runtime
monitors have been developed to detect prediction errors and keep the system in a safe …

Machine learning algorithms are increasingly influencing our decisions and interacting with
us in all parts of our daily lives. Therefore, just like for power plants, highways, and myriad …

Efficiency of security-by-design has become an important goal for organizations
implementing software engineering practices such as Agile, DevOps, and Continuous …

We are living in a golden age of machine learning. Powerful models perform many tasks far
better than is possible using traditional software engineering approaches alone. However …

Recently, machine learning systems with inference engines have been widely used for a
variety of purposes (such as prediction and classification) in our society. While it is quite …

Software architects should specify security measures for a software system on an
architectural level. However, the implementation often diverges from this intended …

Machine learning is commonly being used in almost all the areas that involve advanced
data analytics and intelligent control. From applications like Natural Language Processing …