Scholarlink

<p>Generative AI systems, including large language models and image or video generators, are typically trained on data scraped from the internet. Since the creators of the works used for AI training have often neither consented to nor been compensated for such use, this has led to allegations of “theft”. At the same time, since generative AI models do not store copies of their training data, there is a widespread view that they no not necessarily infringe copyright. Moreover, in human creative practice, drawing inspiration from and learning from the works of others is common, if not unavoidable. The central challenge, then, is to formulate an intellectual property right that allows creators to restrict AI training without limiting human learning and creativity. McIntyre (<CitationRef CitationID="CR9">2026</CitationRef>) proposes new strategies for addressing this challenge, most notably through an argument relying on proprietary rights. Critically engaging with this approach, I argue that it overlooks the power dynamics in the art world and leaves creators in a vulnerable and dependent position. Instead, I outline a different pathway to restricting AI training that centres on what I call “inspirational responsibility”. This concept captures the responsibility creators bear for how they use and engage with the work of others. Since human creators can be held inspirationally responsible, whereas AI models cannot, the notion of inspirational responsibility provides a normatively relevant basis for restricting AI training without imposing parallel constraints on human learning and creativity.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Inspirational Responsibility, Intellectual Property, and AI Training

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月1日

查看全文
  • Jonas Bozenhard

摘要

Generative AI systems, including large language models and image or video generators, are typically trained on data scraped from the internet. Since the creators of the works used for AI training have often neither consented to nor been compensated for such use, this has led to allegations of “theft”. At the same time, since generative AI models do not store copies of their training data, there is a widespread view that they no not necessarily infringe copyright. Moreover, in human creative practice, drawing inspiration from and learning from the works of others is common, if not unavoidable. The central challenge, then, is to formulate an intellectual property right that allows creators to restrict AI training without limiting human learning and creativity. McIntyre (2026) proposes new strategies for addressing this challenge, most notably through an argument relying on proprietary rights. Critically engaging with this approach, I argue that it overlooks the power dynamics in the art world and leaves creators in a vulnerable and dependent position. Instead, I outline a different pathway to restricting AI training that centres on what I call “inspirational responsibility”. This concept captures the responsibility creators bear for how they use and engage with the work of others. Since human creators can be held inspirationally responsible, whereas AI models cannot, the notion of inspirational responsibility provides a normatively relevant basis for restricting AI training without imposing parallel constraints on human learning and creativity.