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

TL;DR

This paper examines the gap between recommended stakeholder involvement for responsible AI and actual industry practices, analyzing guidance documents and practitioner insights to identify disconnects and propose improvements.

Contribution

It provides a detailed analysis of existing SHI guidance and compares it with real-world practices, highlighting key disconnects and suggesting targeted interventions.

Findings

SHI in practice is driven by commercial priorities.

Current practices often do not align with rAI goals.

Several factors discourage rAI-aligned stakeholder involvement.

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.