The paper presents LAAF, a Layered Accountability Architecture Framework for Large Language Model (LLM) applications, developed through a systematic review of 122 primary studies and 12 regulatory documents. It identifies five dimensions of accountability and four families of mechanisms—technical controls, human oversight, organisational governance, and documentation/traceability—each assessed for maturity. The framework is mapped onto major regulatory standards (EU AI Act, NIST AI RMF, ISO/IEC 42001) and highlights persistent gaps such as under‑specified human oversight and lack of shared accountability metrics.
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.
By Fola Adeleke, Rachel Adams, Ayantola Alayande, Daniela Benavente, Ana Florido, Nicol\'as Grossman, Leah Junck
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.
By Nitesh V. Chawla, Paulo Benanti
arXiv:2606. 13039v1 Announce Type: cross Abstract: The UK government has adopted a pro-AI stance to help transform public service delivery in the face of severe financial pressures, but the path to translate this vision into responsible AI practice remains ill-defined.
By Sitong Lyu, Shabnam Taghiyeva, Mohit Kukadia, Denis Newman-Griffis
arXiv:2606. 12415v1 Announce Type: cross Abstract: The rapid global expansion of artificial intelligence regulation has generated, across multiple jurisdictions, a demand for legal expertise dedicated to AI that the market has addressed in a fragmented manner.
By Nicola Fabiano
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.
By Josephine Schwab, Nathan Naidoo, Ferruccio Barazzutti, Sheryn Lee, Caio Vieira Machado
The paper introduces TRACE, a seven-instrument governance framework designed to measure and manage Explainability Debt in production AI systems. Its core metric, the Explainability Debt Score (EDS), tracks the proportion of decisions falling below a defined explainability confidence threshold, while complementary tools such as DART, SHIV, FDE, HVE, AIDE, and ZERO support forecasting, daily validation, drift evaluation, human oversight, audit decisions, and remediation. A twelve‑month case study on a fraud‑detection system demonstrated that TRACE could predict EDS levels months in advance and reveal risk asymmetries invisible to traditional performance metrics.
By Harish Kant Pathak
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.
By Oier Mentxaka, Natalia D\'iaz-Rodr\'iguez, Mark Coeckelbergh, Marcos L\'opez de Prado, Emilia G\'omez, David Fern\'andez Llorca, Enrique Herrera-Viedma, Francisco Herrera
arXiv:2608. 02699v1 Announce Type: new Abstract: When algorithms make or influence consequential decisions---about loan eligibility, hiring, or healthcare---EU law grants affected individuals a Right to Explanation.
By Benjamin Fresz, Elena Dubovitskaya, Marco F. Huber
The paper introduces Governance-as-Code (GaC), a framework that translates the EU AI Act’s technical requirements into 43 machine‑checkable acceptance criteria across six compliance modules. GaC runs within a CI/CD pipeline, producing Article‑indexed audit evidence and providing actual Rego policy code. The authors validate GaC on two enterprise deployments, showing it reproduces manual audit findings—including three penalty‑triggering violations—while reducing audit labor by about 75%.
By Rudrendu Kumar Paul, Sourav Nandy
arXiv:2606. 14594v1 Announce Type: cross Abstract: AI-assisted software development has moved from line-level autocomplete to agents that can plan changes, edit files, and submit pull requests with limited human supervision.
By Jassem Manita, Aziz Amari
arXiv:2608. 12166v1 Announce Type: cross Abstract: Algorithm registers have been championed as a means of providing transparency on the use of algorithms in public services.
By \'I\~nigo de Troya, Maurus Enbergs, Neelke Doorn, Roel Dobbe