Continuous Auditing of Artificial Intelligence: a Conceptualization and Assessment of Tools and Frameworks

Artificial intelligence (AI), which refers to both a research field and a set of technologies, is rapidly growing and has already spread to application areas ranging from policing to healthcare and transport. The increasing AI …

How to explain AI systems to end users: a systematic literature review and research agenda

Purpose Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users. Design/methodology/approach …

Co-shaping an ecosystem for responsible AI: five types of expectation work in response to a technological frame

Governing artificial intelligence (AI) requires cooperation, although the collaboration’s form remains unclear. Technological frames provide a theoretical perspective for understanding how actors interpret a technology and act upon its development, use, and governance. However, we …

What about investors? ESG analyses as tools for ethics-based AI auditing

Artificial intelligence (AI) governance and auditing promise to bridge the gap between AI ethics principles and the responsible use of AI systems, but they require assessment mechanisms and metrics. Effective AI governance is not only …