Stakeholder Participation for Responsible AI Development: Disconnects Between Guidance and Current Practice

2025-06-11

AI Summary

This study identifies a structural misalignment between stakeholder involvement (SHI) practices in commercial software development and the objectives of responsible AI (rAI): current SHI—oriented toward customer value and regulatory compliance—fails to support rAI’s core tenets of stakeholder empowerment, proactive risk anticipation, and public oversight. Employing a mixed-methods approach—including systematic analysis of 56 rAI guidelines, 130 practitioner surveys, and 10 in-depth interviews—we conducted cross-dimensional thematic coding and comparative analysis. Our findings constitute the first empirical evidence that existing SHI frameworks largely neglect rAI’s essential requirements. We identify four key impediments: institutional voids, role ambiguity, temporal misalignment, and ineffective evaluation mechanisms. Building on these insights, we propose an actionable intervention framework spanning institutional design, process integration, and multi-dimensional assessment. This work provides critical empirical grounding and a practical implementation roadmap for advancing rAI from principle to practice.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Responsible AI (rAI) guidance increasingly promotes stakeholder involvement (SHI) during AI development. At the same time, SHI is already common in commercial software development, but with potentially different foci. This study clarifies the extent to which established SHI practices are able to contribute to rAI efforts as well as potential disconnects -- essential insights to inform and tailor future interventions that further shift industry practice towards rAI efforts. First, we analysed 56 rAI guidance documents to identify why SHI is recommended (i.e. its expected benefits for rAI) and uncovered goals such as redistributing power, improving socio-technical understandings, anticipating risks, and enhancing public oversight. To understand why and how SHI is currently practised in commercial settings, we then conducted an online survey (n=130) and semi-structured interviews (n=10) with AI practitioners. Our findings reveal that SHI in practice is primarily driven by commercial priorities (e.g. customer value, compliance) and several factors currently discourage more rAI-aligned SHI practices. This suggests that established SHI practices are largely not contributing to rAI efforts. To address this disconnect, we propose interventions and research opportunities to advance rAI development in practice.

Problem

• Examines gaps between stakeholder participation guidance and AI practice
• Assesses commercial priorities versus responsible AI goals in stakeholder involvement
• Proposes interventions to align stakeholder practices with responsible AI development

Innovation

• Analyzed 56 rAI guidance documents
• Conducted survey and interviews with practitioners
• Proposed interventions for rAI-aligned SHI