Freedom of Information (FOI) laws are widely adopted to promote transparency, yet their implementation diverges from formal legal expectations. This article examines how variations in FOI requests shape administrative responses and what these patterns reveal about bureaucratic behavior in municipal administrations. Drawing on a randomized field experiment across Belgian municipalities, the study analyzes how public officials respond to identical FOI requests varying by requester identity and legal framing. The findings show that responsiveness is not neutral but varies systematically across request characteristics and contexts. Ordinary citizens receive higher disclosure rates than institutional or advocacy actors, and invoking FOI law increases compliance for some requesters while reducing it for others. These patterns suggest that administrative responses – understood as observable expressions of bureaucratic behavior – are shaped by how officials interpret implications of requests, including perceived risk and accountability. The article advances a behavioral interpretation of FOI implementation as an embedded administrative practice.

This article considers the interpretation of the provisions of the OECD Anti-Bribery Convention that regulate investigation and prosecution of foreign bribery. The central claim is that the function of these ‘enforcement obligations’ is to prevent under-enforcement, discriminatory enforcement or concealed enforcement. Interpretation should involve close attention to the specificities of their text as well as the contexts in which the obligations operate, and, when those methods fail, their functions. These claims are illustrated through a study of the application of the provisions to Canada’s handling of the SNC-Lavalin case.

Content-moderation transparency suffers from a fundamental reliability problem: platforms disclose information about their practices, but the accuracy of those disclosures is difficult to verify. The Digital Services Act (DSA) introduced a Database of Statements of Reasons that has collected billions of decision-level records since February 2024, and, since July 2025, the Implementing Regulation has harmonised transparency-reporting templates and aligned categorisation across instruments. This article assesses whether those reforms convert the Database from an observation tool into a verification tool. The article argues that while the reforms deliver semantic alignment and comparability, they leave the legal relationship between aggregate reports (Articles 15, 24) and decision-level data (Article 17) undefined. A narrowly tailored duty of traceability – linking reported aggregates to the corresponding statements of reasons – would enable systematic consistency checks without expanding the scope of reporting. Absent this clarification, transparency will remain descriptive rather than verifiable, particularly for intermediaries outside the audit regime.

Toxics information disclosure programs have been hailed by some as being among the most successful environmental programs in history. Yet critics have argued that these policies may invite abuse by reducing traditional regulatory monitoring and enforcement, leading to poorer policy outcomes. Leveraging variation in U.S. state toxics transparency programs, we assess these conflicting claims. We find that information disclosure policies are associated with differences in agency monitoring and enforcement actions as well as with emission levels, but that these relationships are conditioned by the severity of the pollution problem. Overall, our evidence suggests that public information policies and traditional forms of regulation are imperfect substitutes, and successfully reducing toxic emissions requires both policy tools working in concert.

The UK and Australian Modern Slavery Acts require large corporations to disclose annually how they address modern slavery risks in their operations and supply chains. Existing methods assess only a small proportion of these disclosures, or narrowly against explicit legal criteria, overlooking deeper indicators of corporate commitment. This paper presents AIMS-QA, an AI-driven framework for assessing modern slavery statements at scale and beyond compliance. It is built on a structured taxonomy synthesising 13 benchmarking methodologies from academic and civil-society analyses, consolidating 473 metrics into 209 streamlined questions across 115 thematic topics. From these, a 37-question priority framework captures the most widely used indicators while retaining key statutory requirements. The seven reporting criteria of the Australian Modern Slavery Act provide the analytic anchor, with each question tagged as either a statutory baseline requirement (reflecting jurisdiction-specific legal obligations) or a beyond compliance expectation (reflecting stakeholder, investor, and civil-society priorities). The paper then introduces a two-stage AI-driven question answering pipeline. The first stage uses a fine-tuned classification model to extract relevant evidence, which is passed to a large language model, guided by domain-specific prompts, to generate targeted answers. A second-stage retrieval-augmented generation (RAG) Watchdog intervenes where the classifier finds insufficient evidence or the language model cannot answer confidently. Designed for triage and screening rather than enforcement, with human-in-the-loop oversight, AIMS-QA gives policymakers, regulators, and stakeholders a research-informed means of strengthening the monitoring and impact of modern slavery legislation.

Reference: Case C-155/24, Nederlandse Voedsel- en Warenautoriteit and Others v Stichting Rookpreventie Jeugd [2026] EU:C:2026:327 Headnote: Based on the principle of the rule of law set out in Article 2 TEU, international standards referenced in an EU legislative act, being binding, must be freely accessible via a system that is “general, effective, without charge and non-discriminatory.” The costs for setting up such a system must be borne by the European Union. Legislation: Article 4(1) Directive 2014/40 of the European Parliament and of the Council of 3 April 2014 on the approximation of the laws, regulations and administrative provisions of the Member States concerning the manufacture, presentation and sale of tobacco and related products and repealing Directive 2001/37/EC.

Health technology assessment (HTA) has advanced rigorous, transparent methods for informing resource allocation; however, persistent inequities, declining trust, and rapidly evolving technologies expose a gap between technical excellence and social justice. This perspective synthesizes Plenary Three of the HTAi 2025 Annual Meeting, which examined how HTA can contribute to more transparent, equitable, and fair health systems. Drawing on multistakeholder perspectives from patient advocacy, global health equity, health policy, industry, and HTA research, this article argues that transparency, equity, and trust must be treated as foundational principles rather than optional enhancements. It highlights persistent challenges, including opaque deliberation, context-dependent definitions of equity, limited feedback to patient contributors, and evidence bases that insufficiently reflect diverse populations. Emerging practices from leading HTA bodies illustrate practical pathways forward. The article concludes that HTA’s convening power can help transform it from a technical gatekeeper into a guardian of fairer health systems across diverse contexts.

The European Union’s Artificial Intelligence Act changes the governance environment in which natural language processing (NLP) systems are developed and deployed. Its central lesson for Responsible NLP is that responsibility does not follow automatically from a model’s architecture or benchmark score. It is distributed across three layers: a general-purpose model, the task-specific system built around it, and the institution in which that system acts. This piece analyses those layers through recruitment, education, public-service chatbots, public-interest text generation, and general-purpose language models. It shows how the Act turns principles into duties concerning risk management, data governance, documentation, transparency, human oversight, and robustness, together with complaint, explanation, and procedural-accountability mechanisms, while also examining its interaction with data-protection law. The analysis reveals a deeper research challenge: compliance requires an evidence chain connecting intended purpose, affected people, evaluation, institutional workflow, and post-deployment monitoring. Regulation cannot create that evidence by itself. Multilingual fairness, construct validity, meaningful explanation, uncertainty communication, and effective contestability remain scientific and sociotechnical problems. The EU AI Act should therefore be understood as a legal floor for Responsible NLP, not its scientific ceiling.

Open scholarship (OS) is increasingly visible in second language acquisition (SLA) and applied linguistics as a means of supporting transparency, rigor, accountability, and knowledge sharing. In qualitative L2 research, however, the ethical implications of openness depend on context, participants, data type, and conditions of circulation. This critical commentary argues that responsible openness is best understood as circulation ethics, or the ethical reasoning required when qualitative materials move across languages, infrastructures, audiences, and political conditions. Drawing on two vignettes, one involving an externally funded fellowship with Ukrainian refugees in Poland and another involving research connected to Cuba, we distinguish broader qualitative research ethics from concerns generated or intensified by OS. We then develop a relational and CARE-informed framework for evaluating decisions about openness, including decisions to limit, delay, control, or refuse access. Refusal is accountable when it is documented and reasoned, not when it is simply asserted. The commentary proposes an ethical access statement and a decision framework for documenting access decisions related to consent, translation, contextual detail, risk, reuse, and institutional support. We argue that qualitative L2 research can contribute to OS by shifting evaluation from the quantity of materials made public to the accountability of the decisions that govern circulation. Responsible openness requires shared support among researchers, institutions, funders, journals, and reviewers.

This article investigates how to translate abstract responsible artificial intelligence (AI) principles into practical constraints for public-sector AI systems. Focusing on due process, transparency, fairness, reason-giving and proportionality, the paper uses administrative and constitutional law doctrines to scrutinize AI deployments in government. The purpose of this comparative analysis is to evaluate how the divergent frameworks of the United States (US), the European Union (EU) and Singapore fulfill responsible AI principles. The paper contrasts the EU’s unified hard-law model with the fragmented preemption approach of the US and the facilitative soft-law guidelines of Singapore. Furthermore, the analysis explores how legal norms can be integrated throughout the AI lifecycle, addressing the inherent tensions between technical performance and legal compliance. Illustrative case studies – including the Dutch childcare-benefits algorithm, the UK exam-grading fiasco, the US Internal Revenue Service audit controversies and Estonia’s successful Kratt AI implementation – highlight the severe consequences of algorithmic opacity and the benefits of proactive governance. Drawing on these insights, the paper argues that multidisciplinary governance is essential. The article’s original scholarly contribution lies in systematically mapping administrative and constitutional law doctrines onto AI requirements across the AI lifecycle, proposing a concrete techno-legal architecture that ensures automated public decisions comply with enduring legal norms.