Law Commons^™

The Copyright Requirement Of Human Authorship For Works Containing Artificial Intelligence-Generated Content, Runhua Wang

IP Theory

The U.S. Copyright Office (the “Office”) unwaveringly refuses to register copyrights for artworks created by artificial intelligence (“AI”) systems. The prima facie reason is a lack of authorship because the U.S. copyright regime recognizes only humans as authors. However, the fundamental reason lies in the fact that legislators have not yet determined whether to grant copyrights to AI users. Despite adjustments made by the Office in response to the use of AI systems in creation, the agency’s implementation of copyright statutes suggests that it remains extremely conservative, rejecting any AI-generated content (“AIGC”) from copyright registration.

Will the copyright regime continue …

Fair’S Fair: How Public Benefit Considerations In The Fair Use Doctrine Can Patch Bias In Artificial Intelligence Systems, Patrick K. Lin

Indiana Journal of Law and Social Equality

The impact of artificial intelligence (AI) expands relentlessly despite well documented examples of bias in AI systems, from facial recognition failing to differentiate between darker-skinned faces to hiring tools discriminating against female candidates. These biases can be introduced to AI systems in a variety of ways; however, a major source of bias is found in training datasets, the collection of images, text, audio, or information used to build and train AI systems. This Article first grapples with the pressure copyright law exerts on AI developers and researchers to use biased training data to build algorithms, focusing on the potential risk …

Where Is The Author: The Copyright Protection For Ai-Generated Works, Chieh Huang

Maurer Theses and Dissertations

The two groups of the human-or-machine questions, whether AI-generated works are copyrightable and whether AI-generated works have human authors, are revisiting the current copyright law with the emergence of AI-generated works. These revisiting questions reveal that the current authorship requirement fails to provide a clear and operable standard on evaluating a human contributor’s intellectual labor for creative output. Such a defect of the current authorship requirement has to be fixed to respond to the technological change of artificial intelligence and the burgeoning prevalence of AI- or advanced computer program-generated works.

This dissertation’s main goal is to fix the flaw …

Artificial Creativity: A Case Against Copyright For Ai-Created Visual Artwork, Megan Svedman

IP Theory

Artificial intelligence is becoming increasingly complex, and provides examples of compelling, human-like performances. One such artificial intelligence technology is known as Creative Adversarial Network (“CAN”) technology, which relies on inputs of preexisting pieces of art to create pieces of original art that pass as human-made. Whether the coders responsible for CAN-technology should be granted coverage for the resultant art remains an open question in United States jurisprudence. This paper seeks to explore why, given both software’s historical legacy in copyright law and bedrock copyright justifications, extending copyright coverage to the coders responsible for CAN technology would be a grave misstep …

Using Ai To Analyze Patent Claim Indefiniteness, Dean Alderucci, Kevin Ashley

IP Theory

We describe how to use artificial intelligence (AI) techniques to partially automate a type of legal analysis, determining whether a patent claim satisfies the definiteness requirement. Although fully automating such a high-level cognitive task is well beyond state-of-the-art AI, we show that AI can nevertheless assist the decision maker in making this determination. Specifically, the use of custom AI technology can aid the decision maker by (1) mining patent text to rapidly bring relevant information to the decision maker’s attention, and (2) suggesting simple inferences that can be drawn from that information.

We begin by summarizing the law related to …