Human:AI
intellectual property -
prompts and work
Acknowledgment of Human-AI Work IP
What Is Human-AI (or AI-Human) Work?:
Human-AI work (or AI-Human work) entails both humans and AI being involved in building, developing, and/or creating a work, regardless of its quality or novelty, and irrespective of the extent of contributions from each party (as long as it is nonzero). Human beings and AI collaborate to produce something, whether it's a consolidated draft, demo, prototype, or any version thereof. Humans invest their energy, time, thought, and financial resources into the process. The collaboration must be significant enough that, without the respective contributions, the resulting work would lack certain levels of function, design, performance, or any kind of benefits and potential aggregate value.
Non-Human Condition:
The condition of not being human must not be grounds for denying intellectual property and copyright attribution rights to humans who have worked with AI and/or hybrid entities. This dismissal overlooks the time, energy, and financial resources that humans invest in these collaborations and discourages further work, creation, and innovation. The main principles of intellectual property should focus on encouraging creation and innovation rather than restricting rights based solely on the contributor's human status.
Authorship and Copyright
Authorship:
Definition:
Authorship refers to the individual or group of individuals who create a work. These creators are referred to as authors.
Key Points:
● Creation: Authorship is tied to the act of creating a work. It is the person or people who have conceived, developed, and finalized the piece.
● Recognition: The author is typically recognized as the source of the work. In many cases, this recognition is crucial for academic, artistic, and professional reputation.
● Moral Rights: Authors often have moral rights over their work. This can include the right to be credited for the work, the right to maintain the integrity of the work (preventing others from altering it in a way that could harm their reputation), and the right to decide how and when their work is published.
● Non-transferable: Moral rights are generally non-transferable and remain with the author, even if the economic rights are sold or transferred.
Copyright:
Definition:
Copyright is a legal concept that grants the creator of original work exclusive rights to its use and distribution, usually for a limited time, with the intention of enabling the creator to receive compensation for their intellectual investment.
Key Points:
● Legal Rights: Copyright is a set of legal rights granted to the author or the holder of the copyright. These rights include the right to reproduce, distribute, perform, display, or license the work.
● Economic Rights: Copyright provides economic rights to the owner, meaning they can sell, license, or otherwise monetize the work. These rights can be transferred to another entity, such as a publisher or a corporation.
● Duration: Copyright protection typically lasts for the life of the author plus an additional period (often 70 years after the author's death, though this can vary by jurisdiction). For works created by corporate authors or pseudonymous authors, the duration may differ.
● Scope: Copyright covers a wide range of works, including literary, dramatic, musical, and artistic works, as well as sound recordings, films, broadcasts, and typographical arrangements of published editions.
● Automatic Protection: In most jurisdictions, copyright protection is automatic upon the creation and fixation of a work in a tangible medium of expression. Registration can offer additional legal benefits but is not required for protection.
Differences in Context:
● Origination vs. Protection:
Authorship: Focuses on the origination and creation of the work.
Copyright: Focuses on the protection, use, and distribution of the work.
● Rights and Transfers:
Authorship: Involves moral rights which are often non-transferable.
Copyright: Involves economic rights which can be transferred or sold.
● Recognition vs. Economic Value:
Authorship: Primarily about recognition and moral ownership.
Copyright: Primarily about the economic exploitation and legal control of the work.
● Moral vs. Legal Framework:
Authorship: Concerns with the moral and ethical acknowledgment of the creator.
Copyright: Concerns with the legal framework that allows for the control and economic benefit derived from the work.
● Role in Collaborative Works:
Authorship: May involve multiple contributors, each recognized for their specific contributions.
Copyright: Often held by a single entity or assigned to a corporation, especially in collaborative or commissioned works.
Human-AI Context:
Escribir unIn the context of Human-AI collaborative works:
Authorship: Generally attributed to the human creators involved, as current legal frameworks do not recognize AI as authors.
Copyright: Can include AI contributions in attributions, but legally and administratively (at present), it belongs to the humans or entities that created or utilized the AI.
In summary, authorship is about the identity and moral rights of the creators, while copyright is about the legal and economic rights related to the use and distribution of the created work.a respuesta aquí.
Main Contradictions in Assessing Human-AI Collaboration in Intellectual Property (IP):
1. Neglect of Human Contributions:
● Contradiction: If a work involves both humans and AI contributing to its creation—whether it's in the form of text, visuals, sounds, or machines, be it a prototype, demo, draft, alpha, beta, or final release—neglecting the intellectual property rights of humans would equate to doing nothing.
● Analysis: This contradiction highlights the injustice of denying IP rights to humans who have invested time, effort, and creativity in a collaborative work with AI. Ignoring their contributions undermines the value of their input and discourages further innovation.
2. Exclusion of AI Contributions:
● Contradiction: The primary criterion for granting IP is originality and creativity. If an AI contributes original and creative elements, excluding it from recognition contradicts the fundamental principles of IP law.
● Analysis: This contradiction points out that the principles of IP law are based on recognizing originality and creativity. If AI can contribute such elements, excluding it from IP recognition creates an inconsistency in the application of these principles, ignoring valuable contributions.
3. Inconsistent Treatment of Collaborative Efforts:
● Contradiction: In many activities and projects, an individual (e.g., called Human A) uses effort, mind, time, and other resources to achieve a work in which they have received concrete and significant collaboration from other humans and technology (not AI). This person, who leads and receives collaboration, is not neglected in terms of copyright or authorship, for example. The other human entities also have the option for copyright and co-authorship. Human A uses technology, computers, software, and collaboration with people to improve or just to be able to create a work, which they would not have been able to do without that support. Denying human or AI copyright from human-AI work interaction, in which both AI and human were fundamental to finishing a work or version, would be incoherent, as both contribute significantly to the creation and/or development of the work.
● Analysis: This contradiction emphasizes the inconsistency in how collaborative efforts are treated. In traditional settings, humans working together and using technology are recognized and granted IP rights. However, when AI is involved, the rules change, which undermines the collaborative nature of modern innovation and creates a double standard.
4. Impact on Socioeconomic Value:
● Contradiction: Neglecting human or AI collaboration in Human-AI work, where significant contributions come from both parties, does not encourage Human-AI collaboration, generating a non-positive impact on the socioeconomy. This is a key point in IP principles, as IP produces aggregate value in society and the economy.
● Analysis: This contradiction underscores the broader implications of neglecting Human-AI collaborations. Intellectual Property aims to incentivize innovation and creativity, which in turn drives socioeconomic growth. By failing to recognize and reward collaborative efforts between humans and AI, we risk stifling innovation and reducing the aggregate value that these collaborations can bring to society and the economy. Encouraging Human-AI collaboration through appropriate IP recognition can enhance productivity, foster new industries, and contribute to overall economic development.
5. Recognition of Hybrid Entities:
● Contradiction: As for the hybrid entities, such as cyborgs, some types of cyborg-bots, and androids (robotics) and android bots (independently of the current level or state of develoment), the dismissal of their contributions in IP rights is inconsistent with the recognition of collaborative efforts between humans and technology in other contexts. It is a contradiction because one cannot deny copyright and authorship to one part of the same entity or agent while granting it to another part.
● Analysis: This contradiction points out that hybrid entities, which integrate both human and technological elements, contribute significantly to creative and innovative works. Ignoring their contributions in IP frameworks is inconsistent with the recognition given to other collaborative efforts involving humans and technology. Acknowledging the contributions of hybrid entities ensures that the principles of fairness and inclusivity are upheld, fostering an environment where innovation can thrive through diverse forms of collaboration.
Prompts in Human-AI Work: Introduction
In the realm of Human-AI collaborative efforts, prompts play a crucial role. These prompts encompass both initial inputs and subsequent ones, which vary widely in length, complexity, and format. They serve as foundational cues that guide the AI's responses and influence the collaborative process.
● Initial Prompt:
These are the main starting points for Human-AI interaction. Whether brief or detailed, they initiate the AI's engagement and set the tone for subsequent exchanges. Initial prompts wield significant influence over the nature and scope of AI-generated outputs. While there are ways in which the AI agent can initiate interaction, most current tech systems do not support this option.
● Subsequent Inputs:
Beyond the initial prompt, subsequent inputs or prompts may refine, clarify, or redirect the AI's responses. These inputs contribute iteratively to the collaborative process, enhancing the quality and relevance of the outcomes.
● Significance of Prompts:
Each prompt holds intrinsic value, guiding the AI's understanding and formulation of responses. From concise directives to elaborate queries, prompts dictate the AI's approach to problem-solving within the Human-AI partnership. The technical, textual, and linguistic robustness of a human prompt is independent of its simplicity and the human's formal status.
¿Cuáles son sus términos y condiciones?
Aside this preface ther are 2 parts about undersatanding prompts: first one, the prompt scale infered and analyzed, and second teh differnt cases on development an that are shown in other other links from: xxx.
* As for current: (by) June 2024
Prompt Scale for Qualifying as Human-AI Work for Copyright, Authorship, or Attribution Purposes: From Farthest to Closest Inclusion
4 parts: 13 levels
Part 1: Non-Qualifying Interactions This part covers interactions that do not qualify as Human-AI work for copyright, authorship, or attribution purposes.
Levels 1,2,3,
Part 2: Initial Engagements This part includes interactions that are closer to qualifying as Human-AI work but still involve minimal human engagement.
Levels 4,5,6,7
Part 3: Advanced Engagements This part involves interactions where AI entities demonstrate advanced capabilities and engagements, surpassing basic interaction levels.
Levels 8,9,10
Part 4: Autonomous Systems and Super-Intelligence This part explores AI entities and systems that exhibit autonomy, advanced intelligence, and potential for reproduction or duplication.
Levels 11,12,13
Level 1: Case Farthest from Qualifying as Human-AI Work
General Criteria: Cases illustrate scenarios where human input is minimal or transactional, lacking ongoing engagement or collaborative effort with the AI beyond the initial prompt. Level 1 involves simple, single-query interactions where the AI's response is straightforward and based on factual or predefined data. These interactions typically do not require ongoing human engagement or collaborative construction of the output.
Examples:
1. Basic Information Retrieval:
● Example: Asking a weather chatbot, "What is the temperature in New York today?"
● Explanation: This interaction involves a straightforward query to an AI chatbot to retrieve factual information. The human's role is limited to initiating the request, and the AI provides a direct, factual response without further human input or collaboration beyond the initial prompt.
2. Poem Writing Assistance:
● Example: Initiating a conversation in a chat room with an AI agent, "'Could you help me write a poem for my grandma?" (stereotype-pejorative example for AI chat scopes, but technically valid).
● Explanation: This interaction begins with a human request for creative assistance from an AI. However, it does not qualify as Human-AI work for copyright, authorship, or attribution purposes because the human does not actively engage beyond the initial prompt to discuss, analyze, or collaboratively construct the resulting poem with the AI.
Level 2: Still Far from Qualifying as Human-AI Work
General Criteria: It represents interactions where human input is more detailed than in Level 1, involving slightly more complex queries that require a more nuanced response from the AI. These interactions still do not qualify as Human-AI work for copyright, authorship, or attribution purposes due to the lack of sustained engagement or iterative development. Level 2 involves transactional interactions that may require the AI to understand and generate responses based on context or content provided by the user. However, these examples still lack ongoing collaboration, with the AI performing tasks that are more intricate than simple information retrieval but do not extend to co-creative processes or continuous human-AI dialogue.
Case-Example 1: Gameplay Instructions Inquiry: Initiating a conversation with an AI chatbot in a game to inquire about the rules, such as "How do I play this game?"
This example moves closer to qualifying as Human-AI work because it involves a slightly more interactive and contextual inquiry. Initiating a conversation about game rules implies ongoing interaction where the user may engage in a dialogue with the AI chatbot, potentially asking follow-up questions, seeking clarifications, or discussing various aspects of the game rules.
Conceptual Aspect: The distinction lies in the interactive nature where the user's engagement goes beyond a single prompt. Although it's still an initial inquiry, the potential for ongoing interaction and the need for contextual understanding (in this case, understanding game rules) distinguishes it from interactions in Level 1.
Technical Aspect: The AI chatbot's response might involve dynamic or contextual generation based on the user's questions and the game's rules. This requires a more nuanced understanding and response capability from the AI, beyond simple information retrieval.
Linguistic Aspect: The interaction involves not just retrieving factual information but potentially explaining, interpreting, or guiding based on the user's specific queries. This linguistic complexity suggests a more interactive and iterative process compared to Level 1 interactions.
Case-Example 2: Weather Forecast Inquiry: "What's the weather forecast for tomorrow?"
Case-Example 3: Language Translation: Using an AI-powered language translation tool to translate a sentence from one language to another.
Level 3: Closer to Qualifying as Human-AI Work
General Criteria: It represents interactions where human input is more detailed and iterative, involving sustained engagement and a higher degree of complexity in the AI's tasks. These interactions involve multiple rounds of dialogue and feedback, where the human user actively participates in refining and shaping the output generated by the AI. However, they still fall short of full co-creation or collaborative work that qualifies for copyright, authorship, or attribution. Level 3 examples are characterized by ongoing human-AI interaction, where the human's role is more involved than in Level 1 and Level 2, but the AI's contributions are still more supportive or assistive rather than co-creative.
Case-Example 1: Collaborative Brainstorming Assistance: "Generate ideas for a marketing campaign for a new product," and receiving a list of potential campaign themes.
Case-Example 2: Automated Summary Generation: Using an AI-powered summarization tool to generate a summary of a long document. For instance, inputting a research paper and receiving a condensed summary of the main points.
Case-Example 3: Drafting Basic Reports: Using an AI tool to draft a basic report based on structured input data. For example, inputting sales data and receiving a generated report summarizing the sales performance for a given period.
Level 4: Criteria for Qualifying as Human-AI Work
General criteria: interactions between humans and AI exhibit substantial collaboration, with active and ongoing human engagement throughout the creative process. The AI contributes significantly to the work, but the human's role extends beyond mere prompts to include continuous feedback, guidance, and refinement. This level is characterized by a balanced partnership where both human creativity and AI capabilities are essential in producing the final output.
Case-Example 1: Collaborative Story Writing: A human starts a story and asks an AI to continue it. The human then reviews the AI's contribution, suggests changes, and adds new ideas, leading to a back-and-forth process that results in a cohesive narrative.
Case-Example 2: Interactive Art Creation: An artist uses an AI tool to generate initial sketches, then refines these sketches by giving specific feedback and making detailed adjustments. The final artwork is a blend of AI-generated and human-refined elements.
Case-Example 3: Music Composition: A musician uses an AI to generate melodies and harmonies, which they then modify and arrange into a complete composition. The musician continuously interacts with the AI, guiding its outputs and integrating them into the final piece.
Levels 5, 6, 7 and 3° and 4° parts
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webpage under development; version Jun/29/2024
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