News and Research articles on Artificial intelligenceAs the EU shapes its digital regulatory corpus, migrants are left exposed to the structural violence of AI systems deployed at the border.
AI development is concentrated in corporate hands, but community-controlled alternatives ‒ open, efficient, and democratically governed ‒ are already proving viable.
The common call for researchers to simply “translate” sociotechnical insights into policy overlooks the complex dynamics of policymaking. Using two case studies, the article shows how meaningful relationships, not just translation, are key to embedding sociotechnical research in AI governance.
China is recasting artificial intelligence as a tool of infrastructure diplomacy, a strategic shift that confronts the innovation-led paradigm and navigates the risks of fragmented global governance.
What big tech’s latest sustainability reports say (and don’t say) about the true environmental cost of AI.
While transparency is often championed as the key to addressing the risks of automated decision-making (ADM) in public governance, this op-ed argues that a narrow focus on explainability overlooks deeper systemic issues such as power imbalances, commercial influence, and weakened accountability. To address these issues, mechanisms that promote transparency must operate alongside efforts to enhance citizen engagement and other methods of oversight and accountability to better protect democratic values.
The article takes an in-depth look at the AI Act’s governance approach to non-high-risk AI systems and provides a multi-perspective analysis of the challenges that the EU’s regulation of AI brings about.
Klinger & Hacker highlight the risk of “public interest AI” simply becoming a marketing label, despite its potential, due to the inherent misalignment between for-profit goals and public interest aspirations.
The Brazilian Social Security Management Office's AI system reduces the waiting list but increases automatic refusals, harming beneficiaries and increasing inequality in the delivery of public services to the poorest and elderly people.
Is the current regulatory focus on AI misguided? AI-based services are produced in agile production environments that are decades in the making and concentrated in the hands of a few companies. This article illustrates how AI is only the latest output of these production environments, gives an overview of the socio-technical as well as political-economic concerns these environments raise, and argues why they may be a better target for policy and regulatory interventions.
This article explores some conditions and possibilities for public contestability in AI governance; a critical attribute of governance arrangements designed to align AI deployment with the public interest.
Rohde cautions that the economic structure that AI is currently being developed within is unfortunately at odds with the public interest. At the same time, she also believes that intelligent algorithms and digitalisation can in essence contribute to environmental and climate protection in tangible ways.
In this interview Meyer describes her thinking in the role of a funder and enabler of public interest technology, around how they could decide if a project is in the public interest or constitutes a public good, and various interactions between such projects/products and the market, including the importance of free and open source solutions.
Why it does not make sense to move faster when heading the wrong way.
As the debate on public interest AI is still a young and emerging one, we see this special issue as a way to help establish this field and its community by bringing together interdisciplinary positions and approaches.
The principle of proportionality not only addresses the conflict among competing interests under Article 15(1)(h) GDPR but also shapes the justifications for public interest restrictions on the right of access to AI decision-making information.
This article provides an initial analysis of the EU AI Act's approach to general-purpose artificial intelligence, arguing that the regulation marks a significant shift from reactive to proactive AI governance, while concerns about its enforceability, democratic legitimacy and future-proofing remain.
The AI Act will require high-risk AI systems to comply with harmonised technical standards, including for the protection of fundamental rights: what problems might arise when mixing technical standards and fundamental rights?
This paper empirically explores how AWS, Microsoft Azure, and Google Cloud strategically attempt to operationalise infrastructural power in AI development and implementation through their ecosystems for cloud AI.
In the swiftly evolving digital landscape, the advent of generative artificial intelligence (GenAI) is heralding unprecedented changes in how we interact, work, and innovate. However, this technological renaissance brings to the fore a critical yet often overlooked aspect: widening the existing digital divide.