Co-authored by: @davelaurnce

In the contemporary creative landscape, the emergence of Generative AI represents a fundamental change in content creation practices across multiple industries. By utilizing advanced algorithms and extensive datasets, Generative AI autonomously generates a diverse array of content forms, including text and images, by such means revolutionizing traditional workflows and inspiring innovative artistic ventures. However, alongside its transformative potential, the increasing presence of Generative AI raises significant challenges that requires careful consideration.

One of the prime concerns pertains to issues of authorship, originality, and credit allocation. As Generative AI clouds the distinction between human and machine-generated content, determining rightful ownership and attribution becomes increasingly complex. Additionally, the utilization of vast databases in Generative AI processes presents challenges related to database limits and copyright constraints. Accessing, utilizing, and safeguarding these complex repositories of data necessitate careful navigation of legal and ethical frameworks. As such, this article delves into these varied issues, aiming to provide insight into the complexities surrounding Generative AI's integration into contemporary content creation.

AI: Generated or Created?

We’ve all been taught that with great power comes great responsibility, and in the era of technology where it is an ever-evolving superpower, Generative AI is undoubtedly a strong tool. As such, we begin to question where AI's contributions should be credited to. The line is blurred when it comes to proper credit in regards to AI-generated content. The stance in regards to this remains legally and morally ambiguous; is it the creators of the AI that deserve to be credited, or the user who feeds it prompts to generate, or rather, is the AI possible to become a separate entity worthy of credit?

Copyright Issues

Generative AI produced work does not hold copyright claims on any of its products, since the law does not deem it to have human authorship. However, the problem lies with the materials used for the training of Generative AI machines.

Generative AI algorithms trained on copyrighted material without proper authorization may produce outputs that infringe upon the intellectual property rights of the original creators. For instance, if a model trained on copyrighted images generates highly similar visuals, it could lead to copyright violations and legal disputes. Images and other pieces of media may have been used as scraped material to train Generative AI without rightful permissions from its owners. However, with the vastness and anonymity of the Internet it is not easy to trace who would "steal" media from webpages for the purpose of training Generative AI.

Quality Control and Editorial Oversight

Maintaining quality control and editorial oversight is another critical aspect in Generative AI content creation. As AI systems autonomously generate content based on learned patterns and algorithms, there is a risk of producing substandard or misleading content. Ensuring editorial oversight and quality control measures are essential to uphold standards of accuracy, credibility, and relevance in AI-generated content. This involves implementing mechanisms for content validation, fact-checking, and human review to mitigate the risk of misinformation and uphold editorial integrity.

Protection of Rights and Ensuring Fairness

Fair Credit Distribution
To achieve fair credit attribution, it is essential to implement transparent attribution mechanisms that acknowledge the contributions of all involved parties. Ethical guidelines should prioritize transparency and accountability, ensuring that creators retain control over their creations and receive proper recognition for their contributions.

Anti-Generative AI Innovations

Due to the aforementioned issues in regards to Generative AI, there have been innovations made to combat those, such as AI essay checkers, and image cloaking technology (similar as that of anti-facial recognition) in order to protect and prevent theft and unrightful ownership. Though these technologies are currently not perfect, it is still worth checking out or investing in.

Conclusion

Generative AI offers great possibilities and innovation for merging with the creative industry, but also brings up big questions about who's responsible for it. As Generative AI continues to train for the mimicry of human creations, the line between human and machine created contents begin to blur. The laws for AI may not be adapting as fast as AI is evolving, hence it content creators are not wholly protected from any content theft made by Generative AI training. It is currently within the responsibility of AI users and content creators to ensure that all works are fairly credited and with ethical and moral considerations. Generative AI is currently not a replacement for manmade works such as art, however, its best use is of its assistance and efficiency that it may provide for our creators.

References:
https://arxiv.org/pdf/1803.04469
https://www.sciencedirect.com/science/article/pii/S1361841517301135
https://publications.lib.chalmers.se/records/fulltext/252411/252411.pdf
https://guides.lib.usf.edu/c.php?g=1315087&p=9690822#:~:text=a%20consultation%20appointment.-,Using%20Generative%20AI%20and%20Copyrighted%20Works,or%20large%20portions%20of%20text.
https://www.nytimes.com/2023/02/13/technology/ai-art-generator-lensa-stable-diffusion.html