  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.
