System Safety and Artificial Intelligence

This chapter formulates seven lessons for preventing harm in artificial intelligence (AI) systems based on insights from the field of system safety for software-based automation in safety-critical domains. New applications of AI across societal domains and public organizations and infrastructures come with new hazards, which lead to new forms of...

Diagnosing and Addressing Emergent Harms in the Design Process of Public AI and Algorithmic Systems

Algorithmic and data-driven systems are increasingly used in the public sector to improve the efficiency of existing services or to provide new services through the newfound capacity to process vast volumes of data. Unfortunately, certain instances also have negative consequences for citizens, in the form of discriminatory outcomes, arbitrary...

Safety in large-scale socio-technological systems: Insights gained from a series of systems studies

There is still much debate in the safety literature about how to deal with the complexi- ties and dynamics of large-scale socio-technological systems especially. How, for in- stance, comes safety about in ‘high-tech’ complex systems? Also, how should assess- ments of technological (or artefactual) safety be performed in these kinds of systems?...

Fault detection and diagnosis to enhance safety in digitalized process system

The increased complexity of digitalized process systems requires advanced tools to detect and diagnose faults early to maintain safe operations. This study proposed a hybrid model that consists of Kernel Principal Component Analysis (kPCA) and DNNs that can be applied to detect and diagnose faults in various processes. The complex data is...

A systemic hazard analysis and management process for the concept design phase of an autonomous vessel

Autonomous vessels have become a topic of high interest for the maritime transport industry. Recent progress in the development of technologies enabling autonomous systems has fostered the idea that autonomous vessels will soon be a reality. However, before the first autonomous vessel can be released into her actual context of operation, it...

Situating Explainable AI in the socio-technical context: A system safety inspired approach to operationalizing explainability

Explainable AI is the field concerned with trying to make AI understandable to humans. While efforts have resulted in significant improvement in research and practical methods of Explainable AI, there is an urgent need for additional research and empirical studies. The academic research gaps identified in this thesis show that Explainable AI is...

Ripple Effects of Law Execution Automation in Governmental Systems: The Wajong Case

The use of Automated Decision Making (ADM) systems in the public sector will become increasingly prevalent in the future, making citizens increasingly likely to be confronted with decisions that have been made fully automatically, without human intervention. Ever more digitization increases the opaqueness of the social benefits system in the...