arXiv AI
Jul 8
Position: EU AI Act's Research Exemptions Can Break the Publication Norms of Major AI Conferences
arXiv:2506. 03218v2 Announce Type: replace-cross Abstract: The EU has become one of the vanguards in regulating the digital age.
Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap
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.
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arXiv AI
Jul 7
From Regulation to Requirements: An Automated Requirement Derivation and Explanation Pipeline
arXiv:2607. 04448v1 Announce Type: cross Abstract: Ensuring software compliance with regulations such as the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (EU AI Act) poses a significant challenge, as requirements engineers must translate complex legal text into actionable software requirements - a process that remains largely manual and error-prone in practice.
arXiv AI
Jun 12
The AI Legal Specialist: A Juridically Autonomous Professional Profile for AI Governance
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.
arXiv AI
Sep 2
Human-AI Co-Interpretation for Responsible AI: A Hermeneutic Perspective
The paper examines how large language model (LLM) outputs are increasingly used in contexts that demand justified interpretations, such as law, education, policy analysis, and public moral debate. It identifies a recurring failure—interpretive misplacement—where model-generated readings are treated as settled meanings without explicit interpretive frames, provenance, or defensible alternatives, leading to accountability loss. Drawing on philosophical hermeneutics, the author proposes design principles for human‑AI co‑interpretation, reorganizes existing LLM techniques into hermeneutically responsible patterns, and discusses implications for legal practice, education, scholarship, and public discourse, while framing digital hermeneutics as a literacy for critically engaging with AI‑mediated texts.
Hugging Face Trending Papers
Aug 11
Inferential Capability Does Not Determine Legal Scope
Two instruments of EU digital law place inference at their centre and mean different things by it. Article 3(1) of the AI Act uses the capability to infer constitutively: it is the central feature separating the regulated category from conventional software.
arXiv AI
Sep 1
Statutory AI: Aligning Large Language Models With Legal Norms
arXiv:2608.28593v1 Announce Type: new Abstract: With the increasing development of AI regulatory frameworks, ensuring that artificial intelligence systems, particularly generative models, operate in...
arXiv AI
Sep 18
By Their Fruits You Will Know Them: Comparing Formalizations of Law by the Decisions They Encode
The paper introduces a systematic method for comparing different formalizations of the same legal provision by analyzing their inferences on individual cases. It matches formalizations at the node level, derives shared interfaces, and uses a SAT solver to identify edge cases where any two formalizations disagree. The authors apply this approach to ten EU provisions formalized by nine advanced LLMs, finding that behavioral divergence is largely uncorrelated with structural agreement and that the resulting edge cases expose distinct types of disagreement, some reflecting real legal controversies.
arXiv AI
Aug 12
Inferential Capability Does Not Determine Legal Scope
arXiv:2608. 10601v1 Announce Type: cross Abstract: Two instruments of EU digital law place inference at their centre and mean different things by it.
Hugging Face Trending Papers
Aug 27
LAAF: A Layered Accountability Architecture Framework for LLM Applications
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.
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
4d ago
Bathtubs, Boundaries, and Sandboxes: AI Regulatory Learning under Legal Uncertainty
arXiv:2601.04094v4 Announce Type: cross Abstract: Effective regulation of AI is a defining policy challenge, driven by their integration into all aspects of society. To remain responsive to their rap...
arXiv AI
Aug 28
LAAF: A Layered Accountability Architecture Framework for LLM Applications
The paper introduces 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 accountability dimensions and four families of mechanisms—technical controls, human oversight, organisational governance, and documentation/traceability—evaluated across maturity levels. The framework maps onto major standards such as the EU AI Act, NIST AI RMF, ISO/IEC 42001, and sectoral guidance, highlighting gaps in human oversight, accountability metrics, disciplinary alignment, and empirical validation.