Deep neural networks (DNNs) have achieved state-of-the-art results in a broad range of
tasks, in particular the ones dealing with the perceptual data. However, full-scale application …

Responsible innovation on large-scale Language Models (LMs) requires foresight into and
in-depth understanding of the risks these models may pose. This paper develops a …

Large language models (LLMs) may not equitably represent diverse global perspectives on
societal issues. In this paper, we develop a quantitative framework to evaluate whose …

In recent years, the ethical impact of AI has been increasingly scrutinized, with public
scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This …

As the popularity and adoption of Artificial Intelligence (AI) systems continue to rise, this
article presents a promising proposition: the use of AI dialects to enhance AI perception. By …

Large language models and generative AI, such as ChatGPT, have gained influence over
people's personal lives and work since their launch, and are expected to scale even further …

The aim of the paper is to define Artificial Intelligence (AI) for librarians by examining general
definitions of AI, analysing the umbrella of technologies that make up AI, defining types of …

There is a long history of the science of intelligent machines and its potential to provide
scientific insights have been debated since the dawn of AI. In particular, there is renewed …

In the last few years, we have witnessed a surge in scholarly interest and scientific evidence
of how algorithms can produce discriminatory outcomes, especially with regard to gender …

Science-fiction (SF) has become a reference point in the discourse on the ethics and risks
surrounding artificial intelligence (AI). Thus, AI in SF—science-fictional AI—is considered …