用人类的判断力与直觉应对黑箱AI系统的伦理风险
Practical Judgment, Virtue, and Intuition in the Use of Opaque AI-Enabled Systems
- 强调人类实践判断、美德和直觉在使用黑箱AI中的关键作用
- 指出不可量化的道德品质是应对系统不透明的核心
- 以军事领域为例,适用于所有依赖自主系统的场景
AI系统在多个领域广泛应用,但其核心功能常为黑箱,用户无法理解输入如何转化为输出。当这类系统与自主性结合时,引发可靠性、可控性及伦理法律合规性等多重担忧。本文主张通过发挥人类的实践判断、美德与直觉来缓解这些风险。我们强调,许多积极的人类特质无法量化,因此需建立基于人文价值而非数值指标的AI部署训练与规范。文章以军事领域为例,说明这些能力对伦理与有效决策的重要性,但论点可推广至所有部署不透明且可能自主的系统场景(需结合具体领域调整)。
原文摘要 · Abstract (English)
AI-enabled systems are seeing increasing deployment across numerous domains, with many being "black boxes" with respect to core functions and capabilities. I.e., many systems take inputs and give outputs, but without users having any ability to see how the former lead to the latter. AI-enabled systems are also being used to augment autonomy in systems, and autonomy coupled with opacity raises numerous concerns surrounding, e.g., the reliability of systems, their regularity in functioning, human ability to control them, or whether deploying opaque and potentially autonomous systems is in compliance with ethical and legal norms. In this article, we argue that many of these worries can be mitigated by leveraging practical judgment, virtue, and intuition in the deployment and use of opaque AI-enabled systems. We show that focusing on these distinctly human capabilities provides a means for bridging between the practical challenges created by opacity and the ethical, legal, and social norms underpinning particular domains. We argue that a core element in doing this is a recognition that many positive human traits are not quantifiable and we therefore must develop training regimen and guidelines on AI deployment anchored in humanistic but non-quantifiable values. Throughout the article, we focus on the military domain as an exemplar of the importance of practical judgment, virtue, and intuition as drivers for ethical and effective human decision-making surrounding AI deployments, but the underlying arguments apply to all domains where opaque and potentially autonomous systems are being deployed (subject to domain-specific alterations).
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