arXiv AI
Jun 16
How to Detect and Measure the AI Dangers to Democracy
arXiv:2606. 16054v1 Announce Type: cross Abstract: Research on artificial intelligence and democracy has grown quickly over the last decade.
Trustworthy AI and Democracy: A Dual Taxonomy and Operational Framework of Democratic Risks and Contributions
The paper introduces the AIRD/AIPD framework, a dual taxonomy that categorises AI’s democratic risks (threats to autonomy, fairness, public discourse, institutions, and trust) and its positive contributions (enhancing efficiency, accessibility, security, informed citizenry, transparency, accountability, inclusive dialogue, and evidence‑based policymaking). Grounded in democratic principles and the European Commission’s Trustworthy AI requirements, the framework offers an operational process for identifying contexts, classifying effects, mapping to governance mechanisms, and defining responsible actors, illustrated with examples on electoral misinformation and public service allocation.
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arXiv AI
Aug 20
Global Index on Responsible AI 2026 : Conceptual Framework and Methodology
The Global Index on Responsible AI 2026 (GIRAI) 2nd Edition refines its predecessor by distinguishing between framework existence and implementation, expanding from three to five thematic areas, and adding granular variables for framework quality. It evaluates responsible AI governance across five dimensions—Inclusion and Diversity, Ethics and Sustainability, Labour and Skills, Trust and Safety, and Use of AI in Public Service—using 38 indicators organized into three pillars: AI Policy, CSO Engagement, and Enabling Conditions, plus a separate Use of Unacceptable Risk AI penalty. Data from 135 country-level researchers and secondary sources are normalized to a 100-point scale, weighted by pillar importance, and used to facilitate systematic cross‑national comparisons for policymakers, civil society, and AI developers.
arXiv AI
Jul 29
Why Public Service AI Governance Frameworks Risk Failing in the Age of General-Purpose AI: Lessons from Policing
arXiv:2607. 25648v1 Announce Type: cross Abstract: Public services face growing pressure to adopt artificial intelligence (AI) to close the gap between rising demand and falling resources.
arXiv Machine Learning
Sep 11
From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good
The paper argues that AI should be evaluated not only by principles but by concrete protocols that translate commitments into roles, requirements, records, oversight, and assessment. It introduces a rupture test linking institutional baselines to system evaluation, and distinguishes evidence‑bounded deployment from measurement‑bounded governance. The authors propose the RISE AI architecture to make bounded, evidence‑based claims about Responsibility, Inclusivity, Safety, and Empowerment, emphasizing the need for engineering, institutional repair, and ongoing moral judgment.
arXiv AI
Jun 12
Democracy in the Era of Artificial Intelligence
arXiv:2606. 13026v1 Announce Type: cross Abstract: Interfacing Artificial Intelligence (AI) with democracy is one of the most profound challenges of our times.
arXiv AI
Jul 17
Global Index on Responsible AI: 2026 Report
arXiv:2607. 14782v1 Announce Type: new Abstract: Grounded in human rights-based frameworks such as the UNESCO Recommendation on the Ethics of AI, the Global Index on Responsible AI (GIRAI) examines how countries translate responsible AI commitments into enforceable protections, institutional capacity, and redress mechanisms.
arXiv AI
Jun 2
GovAI-Pipe: A Layered AI Governance Pipeline for Citizen-Facing AI in Turkey's e-Government Gateway
arXiv:2606. 01417v1 Announce Type: new Abstract: Turkey's e-Government Gateway (e-Devlet) serves over 68 million registered users with more than 9,200 government services, and is increasingly integrating artificial intelligence into citizen-facing applications such as chatbot assistants and eligibility assessments.
arXiv AI
Sep 15
From Legal Text to AI-specific Risk Sources: A Systematic Analysis of the EU AI Act's High-Risk Requirements
The paper systematically classifies the EU AI Act’s high‑risk requirements, finding that only a minority directly address AI‑specific risk sources while most impose organizational and documentation obligations. From these risk‑related requirements, the authors derive a consolidated list of distinct AI‑specific risk sources, creating an EU AI Act Risk Source List. This list aims to bridge the gap between legal obligations and AI risk‑management practice by providing a structured reference for comparing the Act’s implicit risk coverage with existing AI risk taxonomies.
arXiv AI
Jun 17
From Democracies to Autocracies: How AI Systems Enable Authoritarianism by Design
arXiv:2606. 17286v1 Announce Type: cross Abstract: AI-enabled authoritarianism is not confined to autocracies.
arXiv AI
Jul 20
Closing the AI Trust Gap: The Case for Independent Certification for Trustworthy AI
arXiv:2607. 15992v1 Announce Type: new Abstract: Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings.
arXiv AI
Aug 21
Mapping General-Purpose AI Governance in Twenty AI Middle-Power Jurisdictions
arXiv:2608. 19278v1 Announce Type: cross Abstract: The most capable general-purpose AI (GPAI) models are mostly built in two jurisdictions, the United States and China, but the risks they carry land globally.
arXiv AI
Jun 30
High-Risk AI Systems and the Problem of Identity in the European AI Act
arXiv:2605. 23922v2 Announce Type: replace-cross Abstract: The EU Artificial Intelligence Act (AIA) establishes a lifecycle governance regime for high-risk AI systems built around ex-ante conformity assessment, post-market monitoring, and re-assessment upon "substantial modification.