News and Research articles on Artificial intelligence

Focusing on recommender systems used by dominant social media platforms as an example of high-reach AI, this study explores the directionality of transparency provisions introduced by the Digital Services Act and highlights the pivotal role of oversight authorities in addressing risks posed by high-reach AI technologies.

Follow along as Members of the European Parliament navigate bias and discrimination in AI and explore their perspectives on regulatory measures, shedding light on the complexities of their understanding and paving the path towards informed policy development.

Introduction Globally, there are now over 800 AI policy initiatives, from the governments of at least 60 countries, with most being introduced after 2016. The United Kingdom (UK) is at the forefront of AI governance efforts, at least quantitatively, being second only to the United States (US) in terms of the number of national-level AI policies released (OECD.AI, 2021) and ranking top for the number of mentions of AI in legislative documents between 2016 and 2021 (Zhang et al., 2022). According to the OECD, the US has released 55 documents, the UK 53, the EU 59, and China 22. These figures do not evidence the UK producing better outcomes than other countries that have published fewer …

News media discourses on datafication and automation have become more sensitive to data risks, but their complexity is a challenge for informing lay audiences about root causes and solutions.

This article critically examines how three AI initiatives articulate corporate responsibility for human rights regarding long-term risks posed by smart city AI systems.

The article identifies critical blindspots in current European AI policies and explores the impact of AI technologies in the media and communications sector, based on a novel multi-level analytical framework.

Feminist theories have extensively debated consent in sexual and political contexts. But what does it mean to consent when we are talking about our data bodies feeding artificial intelligence (AI) systems?

This paper introduces a socio-technical typology of bias in data-driven machine learning and artificial intelligence systems. It argues that a clear distinction must be made between different concepts of bias in such systems in order to analytically assess and politically critique these systems. By analysing the controversial Austrian “AMS algorithm” as a case study among other examples, this paper defines the following three types of bias: purely technical, socio-technical, and societal.

This analysis of digital technologies aimed at supporting survivors of sexual and gender-based violence illustrates how they reaffirm normative whiteness.

This article considers the use of AI border control technologies to manage security risks and the gendered, racialised, and sexualised impacts this may have on both regular travellers and asylum seekers.