Scenario-based sociotechnical envisioning (SSE): an approach to enhance systemic risk assessments

Regulators and policy makers around the world have endorsed risk-based approaches for governing the challenges and opportunities from Artificial Intelligence (AI). In theory, these assessments should protect the public from the harmful impacts of AI, thus safeguarding important societal values such as the realization of fundamental rights. However, as an anticipatory decision-making tool, the success and legitimacy of risk-based regulatory approaches depend critically on methods that help policymakers and societal stakeholders anticipate how AI may impact a diverse citizenry, the many different ways in which technological change may unfold, and the factors that lead to desirable and less desirable visions of the future. In this paper, we first point out critical flaws in existing assessment methods and regulatory approaches and consequently identify the need to complement current approaches to risk assessment. We call on scholars, policy makers, and the technology industry to engage in anticipatory approaches that go beyond compliance and checklist exercises and acknowledge the responsibility they have for impacts that go beyond what is known. Second, we propose a concrete empirical approach to address our concerns: Scenario-Based Sociotechnical Envisioning (SSE). By using SSE, practitioners can make assessments more inclusive, more aware of the socio-technical context in which AI systems are deployed, and more prospective in terms of identifying impacts before they occur. As part of our article, we also provide examples and a concrete guidebook for scientists, policy makers and legislators wishing to use SSE as one method in their assessment toolbox. Third, we anticipatepossible objections to our approach and proactively discuss how these can be met.

Recommended citation: Kieslich, K., N. Helberger & Diakopoulos, N. (2026). Scenario-based sociotechnical envisioning (SSE): an approach to enhance systemic risk assessments. AI and Ethics. https://doi.org/10.1007/s43681-026-01084-5