Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration
- URL: http://arxiv.org/abs/2604.21554v1
- Date: Thu, 23 Apr 2026 11:27:36 GMT
- Title: Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration
- Abstract summary: Under the EU AI Act, translating AI governance requirements into software development practice remains challenging.<n>We present a legal-text-to-action pipeline that translates EU AI Act requirements into actionable strategies.
- Score: 3.710315394842565
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Under the EU AI Act, translating AI governance requirements into software development practice remains challenging. While AI governance frameworks exist at industry and organizational levels, empirical evidence of team-level implementation is scarce. We address this "Last Mile" Challenge through insider action research embedded within an AI startup. We present a legal-text-to-action pipeline that translates EU AI Act requirements into actionable strategies through internal expert collaboration by extracting requirements from legal text, engaging practitioners in assessment and ideation, and prioritizing implementation through collective evaluation. Our analysis reveals three patterns in how practitioners perceive regulatory requirements: convergence (compliance aligns with development priorities), existing practice (current work already satisfies requirements), and disconnection (requirements perceived as administrative overhead). Based on these patterns, we discuss when governance might be treated genuinely or performatively. Practitioners prioritize requirements that serve end-users or their own development needs, but view verification-oriented requirements as box-ticking exercises. This distinction suggests a translation challenge: regulatory requirements risk superficial treatment unless practitioners understand how compliance serves system quality and user protection. Expert collaboration offers a practical mechanism for transforming governance from external imposition to shared ownership and making previously invisible governance work visible and collective.
Related papers
- Security, privacy, and agentic AI in a regulatory view: From definitions and distinctions to provisions and reflections [53.468443050795344]
The rapid proliferation of artificial intelligence (AI) technologies has led to a dynamic regulatory landscape.<n>As AI paradigms shift towards greater autonomy, specifically in the form of agentic AI, it becomes increasingly challenging to articulate regulatory stipulations.<n>This paper reviews the evolving European Union (EU) AI regulatory provisions via analyzing 24 relevant documents published between 2024 and 2025.
arXiv Detail & Related papers (2026-03-19T13:50:52Z) - Mirror: A Multi-Agent System for AI-Assisted Ethics Review [104.3684024153469]
Mirror is an agentic framework for AI-assisted ethical review.<n>It integrates ethical reasoning, structured rule interpretation, and multi-agent deliberation within a unified architecture.
arXiv Detail & Related papers (2026-02-09T03:38:55Z) - A five-layer framework for AI governance: integrating regulation, standards, and certification [0.6875312133832078]
The governance of artificial iintelligence (AI) systems requires a structured approach that connects high-level regulatory principles with practical implementation.<n>Existing frameworks lack clarity on how regulations translate into conformity mechanisms, leading to gaps in compliance and enforcement.<n>A five-layer AI governance framework is proposed, spanning from broad regulatory mandates to specific standards, assessment methodologies, and certification processes.
arXiv Detail & Related papers (2025-09-14T16:19:08Z) - Can AI be Auditable? [3.0260353258798625]
Auditability is the capacity of AI systems to be independently assessed for compliance with ethical, legal, and technical standards.<n>The chapter explores how auditability is being formalized through emerging regulatory frameworks, such as the EU AI Act.<n>It analyzes the challenges facing AI auditability, including technical opacity, inconsistent documentation practices, lack of standardized audit tools and metrics.
arXiv Detail & Related papers (2025-08-30T18:03:20Z) - Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
arXiv Detail & Related papers (2025-08-12T09:42:56Z) - Limits of Safe AI Deployment: Differentiating Oversight and Control [0.0]
"Human oversight" risk codifying vague or inconsistent interpretations of key concepts like oversight and control.<n>This paper undertakes a targeted critical review of literature on supervision outside of AI.<n>Control aims to prevent failures, while oversight focuses on detection, remediation, or incentives for future prevention.
arXiv Detail & Related papers (2025-07-04T12:22:35Z) - Bottom-Up Perspectives on AI Governance: Insights from User Reviews of AI Products [0.0]
This study adopts a bottom-up approach to explore how governance-relevant themes are expressed in user discourse.<n> Drawing on over 100,000 user reviews of AI products from G2.com, we apply BERTopic to extract latent themes and identify those most semantically related to AI governance.
arXiv Detail & Related papers (2025-05-30T01:33:21Z) - Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
arXiv Detail & Related papers (2025-03-12T21:39:38Z) - SOPBench: Evaluating Language Agents at Following Standard Operating Procedures and Constraints [59.645885492637845]
SOPBench is an evaluation pipeline that transforms each service-specific SOP code program into a directed graph of executable functions.<n>Our approach transforms each service-specific SOP code program into a directed graph of executable functions and requires agents to call these functions based on natural language SOP descriptions.<n>We evaluate 18 leading models, and results show the task is challenging even for top-tier models.
arXiv Detail & Related papers (2025-03-11T17:53:02Z) - Open Problems in Technical AI Governance [102.19067750759471]
Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI.<n>This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
arXiv Detail & Related papers (2024-07-20T21:13:56Z) - Challenges and Best Practices in Corporate AI Governance:Lessons from the Biopharmaceutical Industry [0.0]
We discuss challenges that any organization attempting to operationalize AI governance will have to face.
These include questions concerning how to define the material scope of AI governance.
We hope to provide project managers, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks with general best practices.
arXiv Detail & Related papers (2024-07-07T12:01:42Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.