Article by Anna Tumadóttir and Sarah Hinchliff Pearson: “When it comes to AI, copyright operates in a landscape that is uneven and often unclear. Because of this, the CC licenses, while still important, are not sufficient to address how content is used in AI systems. You can read more on this here. CC licenses also do not fully capture the range of intentions creators and data holders have in an AI-mediated world.
Across the web, creators, communities, and institutions are turning to multiple forms of defensive enclosure to restrict access. These include:
- Legal (e.g. licensing), such as open access publishers recommending CC BY-NC-ND as a mechanism of control, which ACM now does, which negatively impacts human collaboration.
- Technical (e.g. CAPTCHAs, bot blocking, rate limiting), such as what news publishers are doing, which negatively impacts archiving efforts.
- Financial (e.g. paywalled APIs), such as what X did post-acquisition, which negatively impacts researchers.
The problem is that these tools treat all machine use as the same, regardless of the purpose. In trying to limit large-scale extraction by AI developers, they also block public interest uses like research, preservation, and accessibility.
While our research is ongoing, there are early indications of a more fragmented and potentially shrinking commons, along with a weakening of long-standing public interest protections…(More)”.
