Human-Content-to-Machine-Data_Final - Flipbook - Page 33
This is not protectionism in response to innovation. A reciprocal commons is a catalyst for
innovation. In just over two decades, the CC licenses have enabled open access to almost
50% of all published scientiûc research. Tens of millions of cultural heritage items are openly
available through institutions like Europeana, which alone hosts over 25 million open access
items.107,108 Today, tens of billions of CC-licensed works circulate within the commons,
supported by a global movement dedicated to expanding open knowledge.
Like natural commons—forests, ûsheries, water systems—the digital commons depends on
governance, shared values, and sustained cooperation. These ecosystems survive not
because they are inûnite but because communities agree to nurture them. The digital nature
of our commons doesn9t change that. When we treat openness as a one-way street, even the
most abundant resource can run dry.
What we aspire to co-create, in part through CC signals, is a future where extractive no
longer feels like the most apt description of the relationship between AI and the humans who
create the knowledge that develops and feeds it. Beneûting from the commons at the scale
of AI should come with an obligation to give back. The good news is that the options for
meaningful replenishment are abundant in and of themselves.
Reciprocity means we collectively have a broad sense that the beneûts of training machines
on the collective intelligence of humanity are equitably shared. It means there is
transparency about how AI systems work and what data they use, with strengthened norms
around provenance and attribution. These are signals of respect and renewal, and this is our
vision for a thriving creative commons in the age of AI.
We stand at a generational crossroads in how we engage with human knowledge. We can
either shape a reciprocal relationship between AI and the commons, or we can risk watching
decades of open progress erode.
