Overview

The UCF 4.0 is collaborative in nature. That means that we are targeting users who have the following beliefs:

1. Ethical data usage is paramount in AI, ensuring respect for privacy and individual rights.

2. Data sovereignty is a right, empowering owners to control their data’s usage.

3. Innovation thrives under clear, fair data licensing, stimulating safe and legal AI development.

4. Collaboration is essential, and federated data licensing fosters a community of shared knowledge and resources.

5. Intellectual property protection is vital, and licensing guards these rights in the AI domain.

6. The integrity and quality of data underpin reliable and effective AI technologies.

7. Open access and commercial interests must be balanced for mutual advancement in AI.

8. Fair compensation and recognition for data providers are crucial for a sustainable AI ecosystem.

9. Public trust in AI is built on responsible and transparent data use.

10. Anticipating and adapting to future legal challenges ensures resilience in AI innovation.

We will use these beliefs as the cornerstone for our UCF 4.0 persona we’ve named Alex River (found HERE).

We need to protect their data, their rights, while providing for fair usage of content for AI purposes. With that in mind all of the shared content within UCF 4.0 must follow the tenets set forth in the Federated Data License.

AND, we must provide information about the license, training about the license, and the license itself must be created for each organization that wishes to be a contributor within UCF 4.0 and beyond.

This PRD focuses on these issues.

Background

In the swiftly advancing realm of artificial intelligence (AI), data serves as the cornerstone of ingenuity. Our users, those who believe the tenets above, are spearheading a critical reassessment of data governance in AI, advocating for a Federated Data license. This license stands as a testament to the ethical utilization, equitable distribution, and safeguarding of creative intellect within the AI sector.

Navigating the Terrain of Creative Commons

The spectrum of Creative Commons licenses has historically offered a structured model for content creators to delineate the permissible uses of their works. These licenses, recognized for their adaptability and lucidity, include specific stipulations identifiable by the acronyms BY, SA, ND, and NC. Each plays an essential part in the proliferation and application of creative content.

• The Attribution license (BY) mandates the acknowledgment of the creator, honoring the intellectual labor and originality infused in the work.

• ShareAlike (SA) ensures derivatives remain under similar terms, promoting an environment of collective innovation.

• NonDerivative (ND) prevents alterations to the work’s core essence, preserving the creator’s vision.

• NonCommercial (NC) restricts the use of works for profit, maintaining the works’ integrity for communal benefit.

These licenses have effectively served traditional creative fields, but AI’s unique nature calls for a more bespoke approach—a Federated Data License.

The Imperative for a Federated Data License

AI is intrinsically generative, learning from extant data to forge novel creations. While the Creative Commons licenses safeguard individual rights effectively, they fall short of addressing the distinctive generative aspect of AI. This gap is precisely where the Federated Data License becomes vital.

Enabling Ethical Innovation

For visionaries like Alex Rivera, ethical innovation is imperative. A Federated Data License would lay down a definitive framework for AI developers, allowing ethical use of collective data. It would embody the spirit of BY, attributing the original data sources, while fostering AI’s generative nature under harmonized conditions that resonate with the collaborative essence of SA. This framework would ensure that AI’s new creations honor the contributions of original data curators.

Safeguarding Data Authenticity

In the AI sphere, the sanctity of data is critical. Expanding on the ND clause of Creative Commons, a Federated Data License would ensure AI respects the original data’s integrity, preventing misrepresentation or distortion of the foundational patterns and truths.

Harmonizing Commercial and Public Good

The Federated Data License reconciles the commercialization of AI outcomes with public welfare, as inspired by the NC clause. It would pave the way for monetization that honors both data contributors and the community at large, ensuring just recompense and spurring innovation that propels societal advancement.

Advantages of a Federated Data License

A Federated Data License is not merely an alternative; it is an evolutionary necessity in generative AI for several reasons:

• Adaptability: It would be crafted to acclimate to the dynamism of AI, offering a resilient framework supportive of ongoing innovation.

• Harmonization: A singular license across the AI domain would mitigate the current jumble of licensing terms, simplifying compliance and understanding of data usage rights.

• Clarity in AI Training: It would dispel legal uncertainties in AI model training, emboldening more entities to engage in AI development confidently.

• Ethical Benchmarks: The license would embed ethical norms within AI development, ensuring alignment with societal values.

• Public Confidence: Commitment to ethical practices under such a license would enhance public trust in AI, a crucial element for its broader acceptance.

The Path Forward

Champions like Alex Rivera view the adoption of a Federated Data License as a commitment to a future where AI is a force for human empowerment and innovation is pursued with profound ethical consideration. It is a recognition that the data we harness must be managed with the greatest care and foresight.

Objective

Imagine standing at a crossroads: one path leads toward passive observation, the other toward proactive governance. Adopting a Common Generative License is a decisive step on the latter path, ensuring that your company’s data narrative unfolds on your terms.

With each sentence in this guide, we construct a blueprint for your organization’s data strategy in the AI epoch. We've navigated the technical depths, charted the course of compliance, and emerged with a strategic vision. Now, the quill is in your hand to script the next chapter of your organization's legacy in the digital age.

We will provide:

• A marketing campaign, including SEO, that explains and champions the Federated Data License.

• Various one-pagers and other downloadable materials (such as the license itself) for use by our users.

• An automated link that creates a bespoke version of the license for the user.

• Storage of that license within the UCF 4.0 structure that ties the license to the user’s account for use when sharing content within the UCF 4.0 structure.

Personas

The persona for this is named Alex River and can be found HERE.

Success Metrics

List project goals and the metrics we’ll use to judge success

Current Challenges / Limitations

Landing pages do not exist, nor does the intent score, SEO campaign, blog posts, etc.

The interfaces on all UCF 4.0 products must show attribution (and allow attribution to be added manually for content that is pasted in) and must maintain attribution chains as the content is shared and modified.

The interfaces for all UCF 4.0 products must show the applicable license for that content – to include the common generative license for all material created or transformed by the user organization sharing the data.

Benefits

The common generative license is not just an alternative; it is a necessary evolution. Here’s why it serves as a better fit for the world of generative AI:

1. Adaptability: AI is an ever-evolving field, and the license would be designed to adapt to new technologies and methodologies, providing a sustainable solution that supports continuous innovation.

2. Harmonization: A common license across AI would reduce the fragmentation caused by different licensing terms, making it easier for AI practitioners to understand and comply with the data usage rights.

3. Clarity in AI Training: By establishing clear guidelines for training AI models, a common generative license would alleviate the legal ambiguities surrounding the use of data, thus encouraging more organizations to engage in AI development without fear of inadvertent infringement.

4. Ethical Standards: Such a license would codify ethical standards into the fabric of AI development, ensuring that AI serves the common good and aligns with society's values.

5. Public Trust: By demonstrating a commitment to ethical practices, organizations can foster greater public trust in AI technologies, which is critical for widespread adoption and acceptance.

Subscription / Pricing / Billing Impacts

Organizations that wish to share data must create a Federated Data license prior to sharing anything.

Beta and Early Access

Not necessary but this is something we’ll be sharing with our community as it evolves.

Risks and Assumptions

Delays:

In enterprises and organizations with a strong legal underpinning, employees do mostly not have authority to sign or create any form of legal commitment, including sharing of company intellectual property.

Any form of legal commitment needs to be reviewed and approved by the legal representatives of the company.

• Legal teams typically will ask for changes in almost any terms and conditions. The bigger a company, the more they ask suppliers to conform to their standards, not the other way around.

• Legal needs time to review the documents. Depending on their workload and the priority given to this request, this may add some time to the process.

Lower sharing

Any additional step in a process causes lower conversion and completion rates. No matter how well it is done. This step adds extra ‘work’ for the end-user and likely for the legal department. They might have other priorities or just fail to see the value.

Competition / alternatives

The need we identify is existing and real. There is a risk that other organizations who are in a stronger position to communicate and create a standard, will produce a license with the same goal. Eg OSI is also in the process of creating a definition of open source as it relates to AI.

Milestones and Phases

Product Requirements

Use Cases

Happy Day Scenarios

As a conscientious contributor, I am able to go to the Unified Compliance website and fill out the Federated Data license which allows me to fill in specific information about my organization that I represent. Once the information is filled out and I agree to share content to the community within the UC 4.0 product, I (and anyone in my organization using the same workspace), can then share content. All content that we share will have our unique license attached to that content as well as attribution to my organization.

Rainy Day Scenarios

I forwarded the Federated Data license to my legal department and did not obtain an approval. I emailed and called but I was told they are very busy and I will have to wait.

Requirements

Cost

Estimated effort to fulfill all P1 requirements:

Design and Documentation: 4 weeks

Build Solution: 4 weeks.

Total Duration: 8 weeks.

Estimated Number of resources to fulfill all P1 requirements:

Design Resources: 1.

Build Resources: 3.

Open Questions

List any open questions that come to mind throughout the lifecycle of this project

Out of Scope / Future Functionality

List the known features that are out of scope for this project or might be revisited at a later time.

As is case with the assumptions, it is important to list these out so that architects and engineers can plan accordingly for these later updates.

Impacted Product Components

If this project is a component to other areas or an update to an existing product, specifically call out where this product will interact with other areas.

User Interaction and Design

Link to mockups, prototypes, or screenshots related to the requirements.

Process Flow Diagrams

Links to user journeys, process flow, or other diagrams related to the requirements.

Guides

If there are UI components to this requirement, list the main areas where interactive user guides would be needed.

Additional References

List and link to any other reference sites, documents … that might be important to the reader.