Foundation Models and Fair Use (2303.15715v1)

Abstract: Existing foundation models are trained on copyrighted material. Deploying these models can pose both legal and ethical risks when data creators fail to receive appropriate attribution or compensation. In the United States and several other countries, copyrighted content may be used to build foundation models without incurring liability due to the fair use doctrine. However, there is a caveat: If the model produces output that is similar to copyrighted data, particularly in scenarios that affect the market of that data, fair use may no longer apply to the output of the model. In this work, we emphasize that fair use is not guaranteed, and additional work may be necessary to keep model development and deployment squarely in the realm of fair use. First, we survey the potential risks of developing and deploying foundation models based on copyrighted content. We review relevant U.S. case law, drawing parallels to existing and potential applications for generating text, source code, and visual art. Experiments confirm that popular foundation models can generate content considerably similar to copyrighted material. Second, we discuss technical mitigations that can help foundation models stay in line with fair use. We argue that more research is needed to align mitigation strategies with the current state of the law. Lastly, we suggest that the law and technical mitigations should co-evolve. For example, coupled with other policy mechanisms, the law could more explicitly consider safe harbors when strong technical tools are used to mitigate infringement harms. This co-evolution may help strike a balance between intellectual property and innovation, which speaks to the original goal of fair use. But we emphasize that the strategies we describe here are not a panacea and more work is needed to develop policies that address the potential harms of foundation models.

Intellectual Property Challenges in Foundation Models: An Essay on "Foundation Models and Fair Use"

Foundation models have emerged as critical components in modern AI applications, where they serve as powerful tools trained on large-scale datasets to perform a variety of tasks. However, their reliance on copyrighted material has raised significant intellectual property concerns, demanding a nuanced exploration of legal doctrines such as fair use. "Foundation Models and Fair Use" by Henderson et al. addresses these concerns with a comprehensive analysis, posing important questions about the legality and ethics of using copyrighted data in AI training.

Legal Foundations and Perspectives

At the core of the paper is an examination of fair use under U.S. law, a legal doctrine that permits the use of copyrighted material without explicit permission under certain conditions. The four factors of fair use—purpose and character, nature of the copyrighted work, amount and substantiality, and effect on the market—serve as guiding principles. The authors robustly survey relevant case law to elucidate potential risks and limited protections offered by fair use for foundation models, particularly in generative settings.

Crucially, the paper presents experiments and observations suggesting that contemporary foundation models can generate work significantly similar to copyrighted content, calling into question their transformative nature—a key aspect of fair use. The reported ability to regurgitate portions of Dr. Seuss's "Oh, the Places You'll Go" and portions of code from projects using GPL licenses exemplifies the intricate challenges faced by developers and deployers of these models.

Technical and Policy Recommendations

The paper advocates for a co-evolution of technical mitigations and legal frameworks. It suggests specific strategies such as data filtering, output filtering, instance attribution, differentially private training, and learning from human feedback to align AI systems more closely with fair use. These recommendations aim to not only safeguard AI practitioners from legal action but also ensure ethical use respecting creators' rights.

The authors argue for more sophisticated implementations of these strategies, highlighting research needs such as developing high-level semantic similarity measures and better instance attribution mechanisms. These measures are intended to identify potential copyright infringement risks more effectively, extending beyond the simplistic verbatim match criteria often employed.

Implications and Future Directions

The implications of this work are profound, both in practical and theoretical dimensions. On a practical level, the paper calls for AI researchers and technologists to pursue advanced mitigation strategies actively, pointing to a proactive role in shaping legal precedents possibly preventing extreme shifts in copyright law interpretation. On a theoretical level, it invites a conversation about the underlying goals of copyright law, intellectual property rights, and the balance between innovation and protection.

In conclusion, Henderson et al.'s paper serves as an essential guide at the intersection of AI and intellectual property law. It formulates a call-to-action for researchers to engage in multidisciplinary work that integrates legal, technical, and ethical considerations, thereby fostering a more balanced evolution of foundation models within the intellectual property framework.