Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir"

Abstract

There is a growing consensus in HCI and AI research that the design of AI systems needs to engage and empower stakeholders who will be affected by AI. However, the manner in which stakeholders should participate in AI design is unclear. This workshop paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices via a survey of recent published research and a dozen semi-structured interviews with AI researchers and practitioners. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design and articulates a set of empirical findings that in ensemble detail out the contemporary landscape of participatory practice in AI design. These findings can help bootstrap a more principled discussion on how PD of AI should move forward across AI, HCI, and other research communities.

Pre-print of an accepted paper at the Human-Centered AI workshop at NeurIPS 2021

References in corpus (2)

• Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics
• What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design

Cited by in corpus (5)

• AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia
• Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services
• Deliberating with AI: Improving Decision-Making for the Future through Participatory AI Design and Stakeholder Deliberation
• `It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values
• From Fake Perfects to Conversational Imperfects: Exploring Image-Generative AI as a Boundary Object for Participatory Design of Public Spaces