设计新型内容市场，解决AI训练与创作者激励的矛盾
Market Design for AI: Beyond the Copyright Binary
- 用动态博弈模型分析版权制度对创作激励的影响
- 发现原创性越高越受压制，导致内容同质化加剧
- 提出中介协调+补贴机制，兼顾创新与模型性能
如何设计人类生成内容用于训练AI模型的市场，既能推动技术进步，又能维持高质量内容创作的个体激励？现有方案分为两种极端：基于合理使用的“自由获取”模式，和强知识产权保护模式。我们证明两者均失效：前者不补偿创作者，后者在静态斯塔克尔伯格博弈中削弱创作激励，尤其对创新者造成“原创性惩罚”。扩展至动态模型后，发现另一市场失灵：优质AI模型促使人类更依赖其辅助创作，导致内容趋同并反向输入训练数据，恶化模型性能——即“精准诅咒”。为此，我们提出由数据中介集体谈判并补贴创新贡献的市场设计方案，可恢复效率。
原文摘要 · Abstract (English)
How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual incentives for high-quality content creation? Existing approaches take polar positions: a "free-for-all" model based on fair use and a "strong intellectual property rights" model. We show that both fail: Free-for-all does not compensate creators, and---by modeling as a static Stackelberg game---strong intellectual property rights also underpower creative incentives. We find this especially true for more innovative creators, a phenomenon we term the "originality penalty." Extending this insight to a dynamic model, we find another market failure undermining AI model performance, even for an initially good model: Such a model induces greater reliance by humans on AI-assisted creation, resulting in homogenized content feeding back into training, which degrades the model performance---a "curse of precision." We further propose a market design with a data intermediary negotiating collectively with the AI firm and subsidizing innovative contributions, thus restoring efficiency.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。