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...

Trustworthy AI Governance - Panel Discussion

As AI technology becomes more powerful, the impact on governance and the governance of AI becomes more important. The purpose of this panel is to bring together experts on Trustworthy AI and E-Governance to discuss the lessons learned so far and the challenges ahead. The goal is to provide a comprehensive review of challenges and potential...

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...

Hard choices in artificial intelligence

As AI systems are integrated into high stakes social domains, researchers now examine how to design and operate them in a safe and ethical manner. However, the criteria for identifying and diagnosing safety risks in complex social contexts remain unclear and contested. In this paper, we examine the vagueness in debates about the safety and...