Generative Artificial Intelligence (GAI) has been the talk of the town lately, with applications like Dall-E, Beatoven, Midjourney, and ChatGPT gaining immense popularity. They enable artificial creativity, producing impressive outputs in text, images, music, videos, 3D printing, and more. However, despite its appeal, this technology is not without legal issues.
One of the most pressing concerns is intellectual property. GAI developers may unknowingly infringe on existing copyrighted works during the training process, as they rely heavily on vast amounts of data available on the internet. Several lawsuits have already been filed in the US, questioning this practice and highlighting a lack of consensus on authorship and ownership.
The issue of authorship in GAI outputs also remains unresolved. Since the creative effort does not come from individual authors, it is challenging to establish who owns the copyright. It remains unclear whether the user, developer, or both should be considered the author. Additionally, AI may not qualify as a legal person for copyright purposes, adding further complexity to the matter.
Bias and prejudice in AI are another major concern. Reports suggest that AI exhibits racial, ethnic, and gender bias in its outputs. This poses a significant risk for public-facing AI, with potential reputational damage for businesses and legal consequences in jurisdictions with robust anti-discrimination laws.
Finally, accountability, explainability, and liability are critical issues that GAI developers and businesses need to address. With the potential for unlawful or harmful content, determining who is liable for GAI outputs is a challenge. The black box conundrum makes it difficult for developers and users to predict and understand the final output. This creates accountability issues, particularly for public-facing GAI.
In conclusion, despite its creative potential, GAI presents several legal challenges that need to be addressed. Identifying liability, ownership, and authorship, along with eliminating bias and ensuring accountability, are essential steps in harnessing the full potential of this technology.
