提出AI治理框架,平衡创新与风险。
Responsible AI: The Good, The Bad, The AI
- 基于悖论理论构建AI治理框架,应对价值创造与风险防控的矛盾。
- 指出单纯权衡无法化解矛盾,需动态管理双重压力。
- 为组织提供可落地的治理策略,适合关注伦理与效率的管理者。
人工智能在组织中的快速普及带来了深远的战略机遇,同时也引入了重大的伦理与运营风险。尽管学术界对负责任AI的关注日益增加,现有文献仍零散割裂,或过度乐观强调价值创造,或过于谨慎聚焦潜在危害。本文通过战略信息系统视角,系统整合负责任AI文献,基于悖论理论,提出悖论型负责任AI治理(PRAIG)框架,阐明:(1) AI采纳的战略收益,(2) 固有的风险与意外后果,(3) 能够驾驭这些张力的治理机制。该框架将负责任AI治理概念化为价值创造与风险缓解之间动态管理悖论张力的过程。我们提出正式命题,表明权衡策略只会加剧而非解决这些张力,并建立悖论管理策略的分类体系及相应的情境条件。对实践者而言,本文提供可操作的指导,帮助制定既不抑制创新也不暴露于不可接受风险的治理结构。论文最后提出推进负责任AI治理研究的未来议程。
原文摘要 · Abstract (English)
The rapid proliferation of artificial intelligence across organizational contexts has generated profound strategic opportunities while introducing significant ethical and operational risks. Despite growing scholarly attention to responsible AI, extant literature remains fragmented and is often adopting either an optimistic stance emphasizing value creation or an excessively cautious perspective fixated on potential harms. This paper addresses this gap by presenting a comprehensive examination of AI's dual nature through the lens of strategic information systems. Drawing upon a systematic synthesis of the responsible AI literature and grounded in paradox theory, we develop the Paradox-based Responsible AI Governance (PRAIG) framework that articulates: (1) the strategic benefits of AI adoption, (2) the inherent risks and unintended consequences, and (3) governance mechanisms that enable organizations to navigate these tensions. Our framework advances theoretical understanding by conceptualizing responsible AI governance as the dynamic management of paradoxical tensions between value creation and risk mitigation. We provide formal propositions demonstrating that trade-off approaches amplify rather than resolve these tensions, and we develop a taxonomy of paradox management strategies with specified contingency conditions. For practitioners, we offer actionable guidance for developing governance structures that neither stifle innovation nor expose organizations to unacceptable risks. The paper concludes with a research agenda for advancing responsible AI governance scholarship.
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