Artificial intelligence (AI) is a salient but polarizing issue of recent times. Actors around the
world are engaged in building a governance regime around it. What exactly the “it” is that is …

This systematic literature review synthesizes the conceptualizations of ethical principles in AI
auditing literature and the knowledge contributions to the stakeholders of AI auditing. We …

Large language models (LLMs) represent a major advance in artificial intelligence (AI)
research. However, the widespread use of LLMs is also coupled with significant ethical and …

Academic and policy proposals on algorithmic accountability often seek to understand
algorithmic systems in their socio-technical context, recognising that they are produced by …

Algorithms are ubiquitous and critical sources of information online, increasingly acting as
gatekeepers for users accessing or sharing information about virtually any topic, including …

Recent years have seen the development of many open-source ML fairness toolkits aimed
at helping ML practitioners assess and address unfairness in their systems. However, there …

Ethics-based auditing (EBA) is a structured process whereby an entity's past or present
behaviour is assessed for consistency with moral principles or norms. Recently, EBA has …

This article offers several contributions to the interdisciplinary project of responsible
research and innovation in data science and AI. First, it provides a critical analysis of current …

“AI as a Service”(AIaaS) is a rapidly growing market, offering various plug-and-play AI
services and tools. AIaaS enables its customers (users)—who may lack the expertise, data …

As the use of AI systems continues to increase, so do concerns over their lack of fairness,
legitimacy and accountability. Such harmful automated decision-making can be guarded …