REGULATING AI: LESSONS FROM SCIENTIFIC COMPUTING

• 成果类型: Article
• 署名作者: Wareham, Jonathan; Romasanta, Angelo Kenneth; Priego, Laia Pujol; Osimo, David
• 署名单位: Universitat Ramon Llull; Escuela Superior de Administracion y Direccion de Empresas (ESADE)
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2025/19111
• 发表日期: 2026-06
• 页码: 413-428
• 关键词: Regulation; Explainable AI (XAI); science; Scientific Computing; PHILOSOPHY; DISCOVERY; HISTORY

摘要

Motivated by the perceived importance of explainability to AI regulation, this paper examines the complex relationship between theory, as a proxy for explainability, and computation in scientific practice. Drawing from both historical and contemporary examples of scientific computing, we make three key arguments: First, we show that theory-computation relationships exist in diverse configurations, ranging from theory-foregrounded and theory-backgrounded to more nuanced arrangements where theory is selectively integrated. Second, scientific fields strategically manage the inherent opacity of some computational methods by injecting theory at critical junctures, showing that complete explainability is not always a prerequisite for scientific progress or utility. Third, we argue that these lessons from scientific computing have important implications for AI regulation anchored in explainable AI (XAI). We suggest that general approaches to AI oversight should be complemented with a context-specific focus on how theory and computation interact in different domains. Implications for AI regulatory regimes and Information Systems (IS) research are discussed.