Creative Commons on Preference Signaling
Of Interest to the Information Community
A Position Paper Prepared for IAB Workshop on AI-CONTROL, submitted 2024-08-02
Creative Commons Position Paper on Preference Signals (IAB Workshop on AI-Control, submitted 2024-08-02. (https://t.co/N9b2rM2GOV ) Includes ideas of potential next steps.
— Jill ONeill (@jillmwo) August 24, 2024
More Details From the Creative Commons Position Paper
The Creative Commons position paper is relatively brief (a mere four pages), but the issue of consent in the context of materials used in training AI models is an increasingly important one.
Establishing the Context:
Preference signals for AI are the idea that an agent (creator, rightsholder, entity of some kind) is able to signal their preference with regards to how their work is used to train AI models. Preference signals would represent a range of creator preferences, all rooted in the shared values that inspired the CC licenses. At the moment, preference signals are not meant to be legally enforceable. Instead, they aim to define a new vocabulary and establish new norms for sharing and reuse in the world of generative AI.
What Is Needed to Communicate Agent Preferences:
For preference signals, or any other approach to communicating or controlling how content is used in generative AI training models, to be successful, our community consultations to date posit that the system must:
- Address the lack of transparency within AI models with regards to when and how creator content is being used; 2
- Center human labor and creativity and consider if there is a specific set of activities that need to be protected in the process of human creating and sharing;
- Prioritize AI for the public good and address the risk of a small minority of players capturing the benefits. Increased public investment and participatory governance of AI should be explored as a meaningful part of the solution. Safeguarding public AI infrastructure is also an act of future-proofing and preserving the commons to enable pro-social organizations building the capacity to train AI models to use the commons as training material.
- Support the development of policy frameworks that foster participatory governance. Reliance on commercial players to set forth industry norms that influence the future of the open commons is imprudent and ill-advised
The Creative Commons position paper may be accessed here.