惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

D
Docker
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
C
Cisco Blogs
Scott Helme
Scott Helme
Know Your Adversary
Know Your Adversary
NISL@THU
NISL@THU
C
Cyber Attacks, Cyber Crime and Cyber Security
D
Darknet – Hacking Tools, Hacker News & Cyber Security
C
CXSECURITY Database RSS Feed - CXSecurity.com
S
Schneier on Security
I
Intezer
Spread Privacy
Spread Privacy
AWS News Blog
AWS News Blog
V
Vulnerabilities – Threatpost
Cloudbric
Cloudbric
V2EX - 技术
V2EX - 技术
Google Online Security Blog
Google Online Security Blog
L
Lohrmann on Cybersecurity
Recent Commits to openclaw:main
Recent Commits to openclaw:main
L
LINUX DO - 热门话题
S
Secure Thoughts
T
The Exploit Database - CXSecurity.com
博客园 - 【当耐特】
Recent Announcements
Recent Announcements
Security Archives - TechRepublic
Security Archives - TechRepublic
Stack Overflow Blog
Stack Overflow Blog
罗磊的独立博客
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
K
Kaspersky official blog
阮一峰的网络日志
阮一峰的网络日志
博客园_首页
Latest news
Latest news
B
Blog
F
Full Disclosure
大猫的无限游戏
大猫的无限游戏
博客园 - 叶小钗
L
LangChain Blog
GbyAI
GbyAI
Last Week in AI
Last Week in AI
S
Security Affairs
Apple Machine Learning Research
Apple Machine Learning Research
N
Netflix TechBlog - Medium
Security Latest
Security Latest
Vercel News
Vercel News
Y
Y Combinator Blog
G
GRAHAM CLULEY
S
Securelist
T
Troy Hunt's Blog
Hacker News - Newest:
Hacker News - Newest: "LLM"
雷峰网
雷峰网

Licenses & Tools Archives - Creative Commons

Takeaways from a London Workshop on CC Signals - Creative Commons From Signals to Infrastructure: Strengthening the Commons for the AI Era - Creative Commons From Signals to Infrastructure: Strengthening the Commons for the AI Era - Creative Commons Update on CC Signals: What Changed and Why - Creative Commons Update on CC Signals: What Changed and Why - Creative Commons CC Licenses, Data Governance, and the African Context: Conversations and Perspectives - Creative Commons CC Licenses, Data Governance, and the African Context: Conversations and Perspectives - Creative Commons CC Signals: What We've Been Working On - Creative Commons Integrating Choices in Open Standards: CC Signals and the RSL Standard - Creative Commons Integrating Choices in Open Standards: CC Signals and the RSL Standard - Creative Commons AI and the Commons: A Reading List - Creative Commons AI and the Commons: A Reading List - Creative Commons We Asked, You Answered: How Your Feedback Shapes CC Signals - Creative Commons We Asked, You Answered: How Your Feedback Shapes CC Signals - Creative Commons Why CC Signals: An Update - Creative Commons Why CC Signals: An Update - Creative Commons Introducing CC Signals: A New Social Contract for the Age of AI - Creative Commons Introducing CC Signals: A New Social Contract for the Age of AI - Creative Commons CC Learning and Training: 2024 Year in Review - Creative Commons CC Learning and Training: 2024 Year in Review - Creative Commons
CC Signals: What We've Been Working On - Creative Commons
Annemarie Eayrs · 2025-12-16 · via Licenses & Tools Archives - Creative Commons

As we look back on 2025, it’s clear that the internet as we know it is changing. Technology-enabled access to knowledge should be flourishing. Instead, information is being removed from the web or locked away in walled gardens. We are experiencing a crisis in the commons, driven in part by current AI development practices. New systems are emerging in response—from content monetization schemes and licensing agreements designed to protect large rightsholders, to the ongoing morass of lawsuits about how AI services are using content as data. We are in the midst of a major reconfiguration of how we share and reuse content on the web.

Bird's eye view photo of a small hut and a concrete path through a lush green forest. However, the image is slightly distorted by digital artefacts.
Distorted Forest Path” by Lone Thomasky & Bits&Bäume, CC BY 4.0, remixed by Creative Commons, CC BY 4.0.

CC Signals: A Refresher

It is within this environment that we continue to develop CC signals. 

We introduced the CC signals concept last June during a live webinar, and further explored the motivation behind this work in our report From Human Content to Machine Data. We also shared the outcomes of our open feedback period following the CC signals kickoff. Since then, we’ve been experimenting in partnership with values-aligned stakeholders and developing pilot projects to test ideas raised by the community.

The goal of CC signals is to help creators and custodians of collections express how they want their content or data to be used in AI development in ways that uphold reciprocity, recognition, and sustainability. Today’s AI systems depend on vast amounts of human-created content, often collected without the awareness or involvement of those who made it. This has concentrated power and undermined trust in the social contract of the commons. 

CC signals responds by promoting community agency while preserving Creative Commons’ core commitment to access and openness. Ultimately, through CC signals and other interventions that infuse concepts of reciprocity in standards and practices, we envision an open internet where participation is equitable, creators are respected, and innovation advances the commons—not unchecked extraction.

CC Signals: Where Are We Now?

CC signals is an evolving, values-driven framework—currently being tested through a series of pilot efforts. Our strategy is to explore modular approaches across legal, technical, and normative dimensions to encourage responsible AI development practices. This allows CC signals to adapt as norms, technologies, and standards continue to evolve.  

At present, two key implementations are underway:

  • Implementing CC signals on Mozilla Data Collective: We are working in partnership with our friends at Mozilla, looking at how implementation of CC signals would work on the Mozilla Data Collective platform, which is purpose-built to enable ethical dataset sharing and fair value exchange. Our plan is to test various ways of incorporating some measure of legal enforceability into CC signals. We also hope to use this as an opportunity to test which CC signal elements are most popular and impactful, and which ones have the biggest impact on AI developer behavior. 
  • Adapting the CC signals contribution element in the RSL framework: Using the framework of the ecosystem contribution signal element, we are working with the RSL Collective to embed the notion of reciprocal contribution into this evolving standard. As a platform that will let rightsholders set machine-readable licensing terms for their content, integrating the contribution element ensures that standards such as RSL provide mechanisms for AI developers to contribute back to the commons at the collective or community level, not simply a one-to-one payment. 

Beyond CC signals itself, we are also exploring whether updates to CC’s license infrastructure could further strengthen and support the commons in the age of AI.  

Looking Ahead

We are actively seeking expressions of interest from dataset custodians who are interested in participating in the Mozilla Data Collective pilot project. If that’s you, we’d love to hear from you.  

We are also exploring sector-specific CC signals integrations, particularly within cultural heritage and science. 

Ultimately, CC signals are incarnations of what we want to see in the world—more recognition for authorship, sustainable commons communities, mutual commitments to shared resources. We are focused on building a vocabulary and vision for the values we think a successful commons needs to thrive. 

This work is resource-intensive. We need your support to ensure this work continues to be led by public interest organizations. Please donate today.

Posted 15 December 2025