The Collective Intelligence Project has published a new working paper by Saffron Huang and Divya Siddarth that discusses the impact of Generative Foundation Models (GFMs) on the digital commons. One of the key concerns raised by the authors is that GFMs are largely extractive in their relationship to the Digital Commons:
The dependence of GFMs on digital commons has economic implications: much of the value comes from the commons, but the profits of the models and their applications may be disproportionately captured by those creating GFMs and associated products, rather than going back into enriching the commons. Some of the trained models have been open-sourced, some are available through paid APIs (such as OpenAI’s GPT-3 and other models), but many are proprietary and commercialized. It is likely that users will capture economic surplus from using GFM products, and some of them will have contributed to the commons, but there is still a question of whether there are obligations to directly compensate either the commons or those who contributed to it.In response, the paper identifies three proposals for dealing with the risks that GFMs pose to the commons.
In response, the paper identifies three proposals for dealing with the risks that GFMs pose to the commons. Read the full paper here: