Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration

2026-04-23

AI Summary

This study addresses the persistent challenge of operationalizing AI governance requirements within software development practice, particularly at the team level. Through an embedded action research approach in an AI startup, the authors construct a translational pipeline that bridges regulatory texts and concrete engineering actions. They propose a governance implementation framework grounded in practitioners’ cognitive orientations—convergence, alignment with existing practices, and disengagement—to shift governance responsibility from externally imposed mandates toward collective team accountability. By integrating legal text analysis, cross-functional collaboration, and collective assessment, the project surfaces developers’ authentic attitudes toward regulation, identifies compliance priorities anchored in user and developer needs, and renders implicit governance work explicit and institutionalized.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Humans and AI: Teamwork, Team formation
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Responsible Web: Technology governance, policy, and regulations
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• AI governance
• EU AI Act
• last mile challenge
• regulatory compliance
• software development

Innovation

• AI governance
• EU AI Act
• action research
• expert collaboration
• regulatory compliance