Codifying “by design” principles into policies may lead to contradictions.

What big tech’s latest sustainability reports say (and don’t say) about the true environmental cost of AI.

This op-ed explores how blockchain could catalyse a global, open alternative to the current scientific system.

Given the ubiquity of fairness as a normative criterion in tech policy, this op-ed warns of particular risks to its legitimising potential which may, in the long term, damage the standing of fairness as crowd-pleaser.

Article 10 of the EU’s AI Act puts data governance at the heart of bias mitigation in high-risk AI systems but offers little guidance on implementation. Delegating these challenges to technical standardisation bodies raises both feasibility and legitimacy concerns, posing a significant test for the institutions now tasked with defining AI fairness in practice.

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.

This op-ed defends the Universal Inscrutability Argument by clarifying what legal explainability actually requires: justifying reasons for institutional decisions, not access to individual motivations. The argument holds that legal standards for explainability should be based on the latter, not the former.

Legal AI is full of talk about 'explainability', but most of it is smoke and mirrors. If these systems are to be useful in law, they need more than plausible stories; they need legally sound reasoning and real-world rigour.

European AI regulations often overlook the experiences of marginalised communities disproportionately impacted by algorithmic biases. This op-ed explores how AI-driven tools exacerbate discrimination against immigrants and minority groups, calling for more inclusive policy frameworks.

Employing a scenario-based method, the authors of this op-ed find that generative AI is not a solution, but a symptom of an overburdened and crisis-ridden academic system. The choice ahead, they argue, is clear.