Data is a crucial component of machine learning. The field is reliant on data to train, validate,
and test models. With increased technical capabilities, machine learning research has …

Ethics of Artificial Intelligence (AI) is a growing research field that has emerged in response
to the challenges related to AI. Transparency poses a key challenge for implementing AI …

Value Sensitive Design (VSD) is a framework for integrating human values throughout the
technology design process. In parallel, Responsible AI (RAI) advocates for the development …

Integrating artificial intelligence (AI) has transformed living standards. However, AI's efforts
are being thwarted by concerns about the rise of biases and unfairness. The problem …

With its potential to contribute to the ethical governance of AI, eXplainable AI (XAI) research
frequently asserts its relevance to ethical considerations. Yet, the substantiation of these …

A series of recent papers raises worries about the manipulative potential of algorithmic
transparency (to wit, making visible the factors that influence an algorithm's output). But …

Recent advances in artificial intelligence offer many beneficial prospects. However,
concerns have been raised about the opacity of decisions made by these systems, some of …

Modern society makes extensive use of automated algorithmic decisions, fueled by
advances in artificial intelligence. However, since these systems are not perfect, questions …

While offering the potential to support learning interactions, emerging AI applications like
Large Language Models (LLMs) come with ethical concerns. Grounding technology design …

We present how Web standards and technologies can implement traceability in business
rules. This enhances transparency of business systems with facilitated integration of system …