Fair use in generative AI | What is Fair and What is not

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Some factors that can help determine if an AI's work qualifies as fair use of copyrighted material include:

The purpose and character of the use: Uses for criticism, commentary, news reporting, teaching, and research are more likely to be considered fair use. Commercial uses are less likely to qualify. So if the AI's work is created for research or educational purposes, it has a better chance of being fair use.

The nature of the copyrighted work: Using factual works is more likely to be considered fair use than using creative works like music, movies, books, etc. So if the AI relies more on public domain factual data, that weighs in favor of fair use.

The amount and substantiality of the portion used: Using a small amount of copyrighted work is more likely to be considered fair use than using the work in its entirety. So if the AI only incorporates small snippets or elements from copyrighted works, that leans towards fair use.

The effect of the use upon the potential market: If the AI's work could significantly hurt the market or potential market value of the original work, it reduces the chances of being deemed fair use. But if there is little or no negative market impact, that supports an argument for fair use.

Transformativeness: If the AI's work transforms the original copyrighted work by using it for a different purpose or changing its character, it is more likely to be considered fair use. For example, parodying an existing work could qualify as fair use. But merely republishing or redistributing the original work with minimal changes would not.

Attribution: Properly attributing the original copyrighted work used by the AI also strengthens the case for fair use. Not crediting the source material at all weakens any fair use claims.

So by analyzing these factors around the AI's work and process, one can get a reasonable sense of whether its creations may qualify as fair use of any copyrighted material. But there is always some uncertainty here, and courts can interpret fair use claims differently in different situations.

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    Build Dataproducts

    How Dataknobs help in building data products

    Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.

    Be Data Centric and well governed

    Generative AI is one of approach to build data product

    Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

    Be Data Centric and well governed

    Generative AI is one of approach to build data product

    Generative AI has enabled many transformative scenarios.

    We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products.

    We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

    Spotlight

    Generative AI slides
    Learn generative AI, LLM, foundation model
  • Learn generative AI - applications, LLM, architecture
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  • See best practices for prompt engineering
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  • Evaluate whether you should use out of box foundation model, fne tune or use in-context learning
    •
  • Most important - be aware of concerns, issues, challenges, risk of genAI and LLM
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  • See vendor comparison - Azure, OpenAI, GCP, Bard, Anthropic. Review framework for cost computation for LLM
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