Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making
Julia Barnett, Kimon Kieslich, Natali Helberger, and Nicholas, Diakopoulos

TL;DR
This paper proposes a participatory approach that involves lay stakeholders in identifying and prioritizing AI risk mitigation strategies, aiming to inform and enrich policy-making processes.
Contribution
It introduces a novel participative method that maps stakeholder responsibilities and incorporates lay perspectives to improve AI governance strategies.
Findings
Lay stakeholders prioritize different mitigation strategies than experts.
The approach produces policy fact sheets that are accessible and informative.
Participatory mapping enhances stakeholder engagement in AI risk management.
Abstract
The potential for negative impacts of AI has rapidly become more pervasive around the world, and this has intensified a need for responsible AI governance. While many regulatory bodies endorse risk-based approaches and a multitude of risk mitigation practices are proposed by companies and academic scholars, these approaches are commonly expert-centered and thus lack the inclusion of a significant group of stakeholders. Ensuring that AI policies align with democratic expectations requires methods that prioritize the voices and needs of those impacted. In this work we develop a participative and forward-looking approach to inform policy-makers and academics that grounds the needs of lay stakeholders at the forefront and enriches the development of risk mitigation strategies. Our approach (1) maps potential mitigation and prevention strategies of negative AI impacts that assign…
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Taxonomy
MethodsALIGN
