Many widely used machine-learning models rely on copyrighted data. For instance, Google finds the most relevant web pages for a search term by relying on a machine learning model trained on copyrighted web data. But the use of copyrighted data by machine learning models that generate content (or give answers to search queries than links to sites with the answers) poses new (reasonable) questions about fair use. By not sharing the proceeds, such systems also kill the incentives to produce original content on which they rely. For instance, if we don’t incentivize content producers, e.g., people who respond to Stack Overflow questions, the ability of these models to answer questions in new areas is likely to be lower. The concern about fair use can be addressed by only training on data from content producers that have opted to share their data. The second problem is more challenging. How do you build a system that shares proceeds with content producers?
One solution is licensing. Either each creator individually licenses the content or becomes part of a consortium that licenses data in bulk and then shares the proceeds. (Indeed Reddit, SO, etc. are exploring this model though they have yet to figure out how to reward creators.) Individual licensing is unlikely to work at scale so let’s interrogate the latter. One way the consortium could work is by sharing the license fee equally among the creators, perhaps pro-rated by the number of items. But such a system can easily be gamed. Creators merely need to add a lot of low-quality content to bump up their payout. And I expect new ‘creators’ to flood the system. In equilibrium, it will lead to two bad outcomes: 1. An overwhelming majority of the content is junk, 2. Nobody is gettingpaid much.
The consortium could solve the problem by limiting what gets uploaded but it is expensive to do. Another way to solve the problem is by incentivizing at a person-item level. There are two parts to this—establishing what was used and howmuch and pro-rating the payouts by value. To establish what item was used in what quantity, we may want a system that estimates how similar the generated content is to the underlying items. (This is an unsolved problem.) The payout would be prorated by similarity. But that may not incentivize creators who value their content a lot, e.g., Drake, to be part of the pool. One answer to that is to craft specialized licensing agreements as is commonly done by streamlining platforms. Another would be to price the contribution. One way to price the contribution would be to generate counterfactuals (remove an artist) and price them in a marketplace. But it is possible to imagine that there is natural diversity in what is created and you can just model the marginal contribution of an artist. The difficulty with the marketplace analogy is that there is no one marketplace. So the likely way out is for all major marketplaces to subscribe to some credit allocation system.
Money is but one reason for why people produce. Another reason people produce content is so that they can get rewarded for their reputations, e.g., SO. Generative systems built on these data however have not been implemented in a way to keep these markets intact. The current systems reduce traffic and do not give credit to the people whose answers they learn from. The result is that developers have less of an incentive to post to SO. And SO licensing its content doesn’t solve this problem. Directly tying generative models to user reputations is hard partly because generative models are probabilistically mixing things and may not produce the right answer but if the signal is directionally correct, it could be fed back to reputation scores of creators.
