Redistributing Decision-Making Power and Influence in the AI Ecosystem
A Call for Inclusive Participation of Users and User Organisations in AI Risk Governance
// Authors & Affiliations
Charles Cresson Wood, Esq.
Attorney & AI Risk Management Consultant (Lakebay, WA)
Specialist in AI risk governance and corporate legal duties; author of "Internal Policies for Artificial Intelligence Risk Management."
LinkedIn Profile →
Shivani Parihast
Founder & CEO, Action for Policy and Economic Growth (APEG)
Senior public policy analyst with 18+ years across digital ethics, financial inclusion, and responsible AI advocacy.
LinkedIn Profile →
Subhendu Chand
Director of Public Policy, Quantum Tiger / Global Consulting Head, Wipro
20+ years driving enterprise AI innovation; Chief Policy Advisor at APEG and Global Consulting Head at Wipro Technologies.
LinkedIn Profile →// Executive Abstract
The global discourse surrounding artificial intelligence risk management has been monopolized by four influential cohorts ("The Included"): major foundation model developers, national governments, elite NGOs, and academic labs. This paper exposes the systemic exclusion of the "Not-Yet-Included"—specifically enterprise users, small businesses, non-profits, and civil society organisations whose economic activity finances the AI ecosystem and who absorb downstream risks. We outline legal, corporate, and structural mechanisms to mandate user representation in AI standards setting and regulatory compliance.
Research Methodology & Theoretical Framework
Legal and institutional policy analysis evaluating U.S. Executive Orders, NIST frameworks, European AI Act mechanisms, and multinational corporate governance mandates.
Key Findings & Technical Breakthroughs
Asymmetric Risk Distribution: Foundation developers export operational and legal risk down to end users through disclaimers, while excluding users from model alignment and risk prioritization.
Corporate Fiduciary Vulnerability: Corporate directors and officers face growing liability for AI failures, requiring formal internal policies and participation in risk frameworks.
Global South Underrepresentation: Emerging economy user organizations bear foreign platform dependencies with zero structural input into model safety parameters.
Decentralized Governance Framework: Advocates for user-led oversight bodies, mandatory feedback channels in foundation licenses, and participatory AI risk audits.
Policy & Industrial Implications
Provides corporate boards and policymakers with actionable templates for establishing internal AI governance councils and demanding seat representation at global AI safety forums.
// Subject Classifications & Keywords
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