协调人工智能系统中的创造力和伦理

Joffrey Baeyaert
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引用次数: 0

摘要

人工智能越来越多地嵌入到创意产业中——不仅增加了文化文物,而且是文化文物的起源。然而,虽然生成系统扩大了获取和生产力,但它们也引发了围绕作者身份、代表性、透明度和环境成本的复杂伦理挑战。建立在公平、问责、透明和隐私基础上的通用人工智能伦理框架仍然过于抽象,无法解决人工智能介导的创造力在特定行业的紧张关系。本文提出了多维道德框架(MDEF),这是一个规范性和可操作性的架构,在整个创意管道中整合了五个道德维度——原创性、文化敏感性、偏见、透明度和可持续性。根据跨学科研究、监管缺口和现实世界平台案例研究,MDEF嵌入了具体的工具,如来源管道、基于熵的偏差审计、共同创造日志、碳仪表板和文化否决协议。它进一步介绍了六个量化指标,包括文化覆盖指数、透明度遵从性指数和能源强度得分,每个指标都有自动或人为干预的校准治理阈值。该框架是为模块化部署、参与式监督和迭代改进而设计的,确保了跨监管环境和创造性子领域的适应性。试点和原型已经证明了可行性,包括通过真实性徽章增加信任,通过参与式审计减少代表性伤害。与静态的基于原则的准则不同,MDEF提供了一种生动的、可验证的方法,将人工智能驱动的创造力与道德权利、认知责任和文化多元化结合起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harmonizing creativity and ethics in AI systems

Artificial intelligence is increasingly embedded in the creative industries—not only augmenting but originating cultural artefacts. Yet while generative systems expand access and productivity, they also raise complex ethical challenges around authorship, representation, transparency, and environmental cost. Generic AI-ethics frameworks built on fairness, accountability, transparency, and privacy remain too abstract to address the sector-specific tensions of AI-mediated creativity. This paper proposes the multi-dimensional ethics framework (MDEF), a normative and operational architecture that integrates five ethical dimensions—originality, cultural sensitivity, bias, transparency, and sustainability—across the creative pipeline. Drawing on interdisciplinary research, regulatory gaps, and real-world platform case studies, the MDEF embeds concrete instruments such as provenance pipelines, entropy-based bias audits, co-creation logs, carbon dashboards, and cultural veto protocols. It further introduces six quantitative metrics, including the cultural coverage index, transparency compliance index, and energy intensity score, each with calibrated governance thresholds for automated or human-in-the-loop intervention. The framework is designed for modular deployment, participatory oversight, and iterative refinement, ensuring adaptability across regulatory contexts and creative subfields. Pilots and prototypes already demonstrate feasibility, including increased trust through authenticity badging and reduced representational harm via participatory audits. In contrast to static principle-based codes, the MDEF offers a living, verifiable approach to aligning AI-driven creativity with moral rights, epistemic accountability, and cultural plurality.

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