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# Not All Similarities Are Created Equal:
Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes

Uri Hacohenλ Adi Havivτ∗ Shahar Sarfatyτ Bruria Friedmanλ
Niva Elkin-Korenλ Roi Livniη Amit H Bermanoτ

τ School of Computer Science , λ Faculty of Law , η School of Electrical
Engineering
Tel Aviv University
<EMAIL_ADDRESS>Equal.

###### Abstract

The advent of Generative Artificial Intelligence (GenAI) models, including
GitHub Copilot, Open AI GPT, and Stable Diffusion, has revolutionized content
creation, enabling non-professionals to produce high-quality content across
various domains. This transformative technology has led to a surge of
synthetic content and sparked legal disputes over copyright infringement. To
address these challenges, this paper introduces a novel approach that
leverages the learning capacity of GenAI models for copyright legal analysis,
demonstrated with GPT2 and Stable Diffusion models. Copyright law
distinguishes between original expressions and generic ones (Scènes à faire),
protecting the former and permitting reproduction of the latter. However, this
distinction has historically been challenging to make consistently, leading to
over-protection of copyrighted works. GenAI offers an unprecedented
opportunity to enhance this legal analysis by revealing shared patterns in
preexisting works. We propose a data-driven approach to identify the
genericity of works created by GenAI, employing "data-driven bias" to assess
the genericity of expressive compositions. This approach aids in copyright
scope determination by utilizing the capabilities of GenAI to identify and
prioritize expressive elements and rank them according to their frequency in
the model’s dataset. The potential implications of measuring expressive
genericity for copyright law are profound. Such scoring could assist courts in
determining copyright scope during litigation, inform the registration
practices at the Copyright Offices, allow registration of only highly original
synthetic works, and help copyright owners signal the value of their works and
facilitate fairer licensing deals. More generally, this approach offers
valuable insights to policymakers grappling with adapting copyright law to the
challenges posed by the era of GenAI.

##  1 Introduction

The emergence of Generative Artificial Intelligence (GenAI) models is
fostering a radical transformation in the creative domain. By using GenAI
models, non-professional users can now generate high-quality content such as
text, images, music, or code. At the same time, however, GenAI is also
disrupting copyright law. By reducing the costs and talent barriers associated
with creative production, GenAI has flooded markets with a massive yield of
syntactic content, leaving scholars and copyright regulators to ponder whether
to merit copyright protection to such works Burk (2023); Grimmelmann (2016).
The approach proposed in this paper can help the stakeholders grappling with
this question by informing them of the scope of unprotected elements in
copyrighted works. In addition, numerous lawsuits are emerging, claiming that
GenAI models are “21st-century collage tools” that infringe the copyrighted
works used to train these models v. Stability AI Ltd. (2023); J. Doe 1 v.
GitHub (2022); Dogan and Liu (2005). These pending class action lawsuits will
require courts to decide, first, whether unauthorized use of copyrighted works
to train GenAI models constitutes copyright infringement and second, when does
the output generated by GenAI models infringes the copyright of works included
in the training dataset. Our paper only addresses the latter question.

These challenges put increasing pressure on the legal system to discern which
portions of copyrighted works are protected from unauthorized use and which
portions are not protected and are, therefore, available for others to use.
Copyright law’s ultimate goal is to foster the creation and dissemination of
expressive works by granting authors only limited rights to their respective
expressions Leval (1990). That is because creation never occurs in a vacuum,
and the author always draws upon preexisting materials by adapting them into
an original expression. Consequently, some elements of copyrighted works that
are building blocks of further creation, such as style, ideas, functions, and
methods, are not protected against unauthorized uses Elkin-Koren et al.
(2024). In fact, copyright law encourages users to draw upon these unprotected
materials to create new original works of authorship Litman (1990). Copyright
law’s greatest challenge is in allocating legal entitlements to expressive
works by consecutive authors. This task has always been a challenge because
authorship derives from cultural contexts. Authors routinely engage with
preexisting materials to convey a meaningful message. This task grows far more
complicated when GenAI augments human creativity Grimmelmann (2009).
Generative models draw upon an infinite number of preexisting works created by
an often-unidentifiable multitude of authors. Consequently, allocating legal
entitlements among all possible claimants becomes an insurmountable objective.
For decades, courts (and, to a lesser extent, the Copyright Office) used the
concept of originality and related legal doctrines to calibrate the scope of
legal protection for copyrighted works Miller (2009); Parchomovsky and Stein
(2009). In this article, we argue that GenAI may help the courts in making
such calibrations. Copyright law requires works to be minimally original to be
protectable, but the breadth of their originality will dictate the effective
scope of these works’ protection against allegedly infringing uses. In
copyright infringement litigation, when copyright owners ask the courts to
enforce their rights against alleged infringers, courts implicitly assess the
originality of copyrighted works and delineate their legal protections
accordingly Parchomovsky and Stein (2009). So far, courts have performed this
task on an ad-hoc basis by applying numerous legal doctrines such as the
idea/expression dichotomy, merger, Scènes à faire, substantial similarity, and
fair use Sas v. World (2014). These doctrines are notoriously vague and
unpredictable and, in practice, often lead to the over protection of
preexisting works Gibson (2007); Litman (2008); Tehranian (2007); Samuelson
(1996); Vaidhyanathan (2003). GenAI introduces new opportunities to infuse
legal analysis with quantitative measures. Since GenAI documents the output of
human creativity on an unprecedented scale, it facilitates a systematic study
of the concealed interconnections among elements of expressive works.
Consequently, GenAI may facilitate the development of new and more accurate
measures to identify expressions that have become generic and better appraise
the originality of these works. In this article, we argue that researchers
could leverage the data-driven bias of GenAI machines to inform copyright
infringement disputes and calibrate the scope of copyright protection. “Data-
driven bias” is a fundamental characteristic of machine learning and affects
the way in which GenAI models generalize Liu et al. (2022). The more commonly
expressive features appear in the GenAI models’ training dataset (the more
“generic” they are), the more likely GenAI models are to utilize them when
generating new works. If the models’ datasets reflect the actual cultural
state of specific expressive domains, the models’ data-driven bias could
indicate the scope of legal protection that the law affords to copyrighted
works. Copyright protection does not extend to generic elements that are not
considered original, which, in turn, depends on their prevalence and cultural
embedment. The more ubiquitous the compositions of elements are, and the more
they are absorbed in preexisting works, the less likely they are to be
considered original under copyright law. Thus, the law will consider common
expressive elements unoriginal (narrowly protected), and uncommon expressive
elements original (broadly protected). By harnessing data-driven bias to
identify genericity and assess copyright originality, computer science
research can inform the legal profession across various practical
applications. For example, courts could use genericity findings to delineate
the scope of copyright protection in copyright infringement analysis.
Copyright Offices could use these assessments to decide whether to register
(and afford protection) GenAI works, and copyright owners could use this
assessment when negotiating copyright licenses. In this paper, we define and
describe data-driven bias and explore its implications for copyright analysis.
We also demonstrate why current proposals for applying technical procedures
are misguided and less informative to the legal profession. We further
highlight several ways by which the framework and methodology we propose could
facilitate follow-on research to measure and score copyright originality.

##  2 Related Work

A growing number of researchers in recent years explore how to address legal
problems by applying theories and methods of computer science. This literature
seeks to narrow the gap between the vague and abstract concepts used by law by
applying mathematical models to offer more rigor, coherent and scalable
definitions into issues such as privacy Dwork et al. (2006), or fairness and
discrimination Dwork et al. (2012). The study of copyright by computer science
methods has only emerged recently. Scheffler et al. (2022), for instance,
proposed a framework to test substantial similarity by comparing Kolmogorov-
Levin complexity with and without access to the original copyright work.

In the context of generative models Carlini et al. (2023); Haim et al. (2022),
explore whether generative diffusion models memorize protected works that
appeared in the models’ training set. This can be considered as a preliminary
issue to the problem of establishing copyright infringement. However, as we
discuss in section 4, memorization of input content does not necessarily
equate with copyright infringement. There is also active and thought-provoking
discussion on how machine learning technologies are reshaping our
understanding of copyright within the realm of law. Asay (2020) explores the
question of whether AI system outputs should be subject to copyright.
Additionally, Grimmelmann (2015); Lemley and Casey (2020) explore the
implications of copyright law for literary machines that extract content and
manage databases of information. Other works, like Bousquet et al. (2020);
Vyas et al. (2023) attempt to evaluate copyright infringement in GenAI models
using privacy-like notions. The contribution of these works lies in proposing
a procedure to inform copyright analysis in line with the thesis of this
paper. However, this approach may not scale easily, and it also falls short of
providing nuanced information regarding the level of genericity, which could
be crucial for resolving copyright legal disputes Elkin-Koren et al. (2024).
Another body of related work attempts to assign attribution scores to training
content to understand the contribution of individual (or group) examples to
model predictions Koh and Liang (2017); Ghorbani and Zou (2019); Jia et al.
(2019); Pezeshkpour et al. (2021); Ilyas et al. . These approaches tend to
adopt techniques such as leave-one-out retraining or influence functions to
understand model behavior, fix mislabeled examples, and debug model errors Koh
and Liang (2017). Instant attrition approaches are useful to determine the
source of generated outputs which can inform copyright infringement analysis
or assign credit to contributors Henderson et al. (2023). However, instant
attribution approaches do not apprise the added value of specific outputs
relative to the aggregated knowledge of GenAI models. Most similar to our
approach are previous attempts by computer scientists to harness computational
measurements for assessing creativity Franceschelli and Musolesi (2021). For
instance, Franceschelli and Musolesi (2022) proposed using generative learning
techniques to assess creativity based on Margaret Boden’s definition of value,
novelty, and surprise Boden (2007). They also created a tool that executed
this assessment called DeepCreativity. However, these systems that rely on
philosophical and psychological perspectives do not necessarily align with the
legal principles of copyright law.

##  3 Research Approach

###  3.1 Copyright Objectives

Copyright law seeks “To promote the Progress of Science and useful Arts, by
securing for limited Times to Authors and Inventors the exclusive Right to
their respective Writings and Discoveries” Constitutional Convention (1787).
Accordingly, copyright law incentives creation and dissemination of original
works by granting authors exclusive rights to their respective works maz
(1954). These rights ensure that authors can commercially exploit their works
and sustain incentives to invest in creating future works. However, promoting
progress is inconsistent with granting unlimited rights to control copyrighted
materials. Instead, it often requires setting substantial limits on the rights
granted to authors di Robilant and Syed (2022); Cohen (1998); Lemley (2005).
That is because creative processes are situated in cultural contexts which
involve building upon existing works and ongoing interactions with preexisting
materials.

For this reason, copyright protection extends only to the aspects of
expressive works that the law considers original usc (1990); Feist v. Rural
(1991); Harper v. Nation (1958). Originality requires that expressive subject
matter originates from its authors rather than copied from someone else.
Therefore, facts and discoveries are never eligible for copyright protection
Feist v. Rural (1991). In addition, originality requires that expressive
subject matter contains a modicum of creativity. Therefore, if a work simply
reflects a set of widely used cultural elements, it cannot be reasonably
attributed to any particular author.

The legal standard of creativity is vague. Courts defined creativity only in a
negative manner to include expressions that are not an “age-old practice,
firmly rooted in tradition and so commonplace that it has come to be expected
as a matter of course.” For example, in the seminal case of Feist v. Rural,
the court held that an alphabetical arrangement of telephone subscribers in a
white page directory is insufficiently creative to merit copyright protection,
because it was a “time-honored tradition [that] does not possess the minimal
creative spark required by the Copyright Act and the Constitution.”

Expressive works do not need to meet a high threshold of originality to be
eligible for copyright protection Feist v. Rural (1991); Goldstein (2005). All
expressive works originating with their authors that are fixed in a tangible
medium and has a modicum of creativity are eligible. Nevertheless, the actual
scope of legal protection that the law affords expressive works varies
according to the degree of originality these works embed Hacohen and Elkin-
Koren (2024).

Courts apprise the originality of expressive works and delineate their
copyright protection in the course of infringement litigation. When copyright
owners ask the courts to enforce their rights against alleged infringers,
courts must decide whether the alleged infringers’ unauthorized uses of
copyrighted works constitute copyright infringement. When making such
determinations, courts implicitly consider the originality level of the
copyrighted works Miller (2009); Parchomovsky and Stein (2009). Specifically,
courts engage in “analytic dissection,” based on a framework established in
Computer Associations International, Inc. v. Altai, Inc. com (1992).

The Altai framework requires the courts to separate the copyrighted works into
different levels of abstraction, filter out the non-original material, and
then evaluate the similarity of the remaining original matter of the
copyrighted work against the allegedly infringing works Lemley (2010).

Naturally, the smaller the portion of the original material within copyrighted
works (and the less original that portion is), the less likely the courts to
find unauthorized uses of such works to be infringing Lemley (1995). Thus,
unlike highly original works, which enjoy broad legal protection against
different forms of derivative uses, limitedly original works will enjoy only
narrow legal protection limited to cases of extreme verbatim copying Apple v.
Microsoft (1994).

In practice, both the filtration and the comparison stages of the Altai
framework require the courts to make ad-hoc assessments of notoriously vague
legal doctrines Litman (1990). During filtration, the courts must assess which
elements of the work are non-original (“generic”) because they have merged
with an “idea” or been used long enough to become indispensable Samuelson
(2016). Courts invoke numerous overlapping doctrines such as “merger,” “useful
article,” and “scènes à faire” to make these assessments Sas v. World (2014).
For example, in Acuff-Rose v. Jostens Acu (1998), the Court ruled that the
defendant was free to copy the phrase “You’ve Got to Stand for Something” from
the plaintiff’s song because the phrase was overused to become a “cliché.”
Similarly, in Lotus v. Borland Lot (1995), the Court denied protection to the
menu command hierarchy of the spreadsheet program Lotus 1-2-3. As Justice
Boudin explained, the extensive use of these commends have made them into a
“method of operation,” which is unqualified for copyright protection.

Once the courts finish filtering out unprotected material, they move to the
next stage of the Altai framework: comparing the similarity of the remaining
material to the allegedly infringing work. If the courts find that the two are
“substantially similar,” they will find for infringement unless they decide
that the challenged use is qualified as “fair use” Lemley (2010). When the
courts evaluate “substantial similarity” and “fair use,” they also consider
the originality of the allegedly infringed copyrighted works Lemley (1995);
Apple v. Microsoft (1994); USC (2012). The less original the copyrighted
works, the higher the burden of similarity that the courts will ask plaintiffs
to satisfy in order to prove copyright infringement Lemley (1995). For
example, in Apple v. Microsoft (1994), the court found that Windows interfaces
do not infringe Apple’s copyrights because they are similar but not “virtually
identical” to the Apple Macintosh unoriginal interfaces.

Similarly, the less original the copyrighted works, the more likely the courts
are to find that the unauthorized uses of these works are fair use Lemley
(1995). For example, in Oracle v. Google Oracle v. Google (2021), the court
found that although Google copied verbatim 37 lines of “declaring code” from
Oracle’s Java program, the copying of these “generic” aspects is non-
infringing Oracle v. Google (2021); Menell (1989). As part of the fair use
analysis, courts may also consider the originality of the allegedly infringing
work, not just that of the copyrighted work Lemley (1997). Courts might view
highly original derivative works as “transformative,” which weigh heavily in
favor of finding for fair use Leval (1990); Campbell v. Acuff (1994). Alas, as
with the lack of definition of copyright originality and the scope-delineating
doctrines during the courts’ filtration analysis, the doctrines of substantial
similarity and fair use are also notoriously vague Meurer and Menell (2013).
As multiple copyright scholars explained over the years, the unavoidable
vagueness of copyright scope doctrines is leading to systematic
inefficiencies, over-protection of preexisting works, and chilling of
expressive speech Gibson (2007); Litman (2008); Tehranian (2007); Samuelson
(1996); Vaidhyanathan (2003)

###  3.2 Preliminary Technical Background

In this work, we consider publicly available variants of state-of-the-art
generative models. Specifically, we explore Stable Diffusion for image
generation (Text-to-Image) and GPT for text generation. To that end we first
offer a short technical review of those models.

#### GPT-2:

Large language model (LLM) Brown et al. (2020) trained on a massive corpus of
text and code, and can generate text, translate languages, write different
kinds of creative content, and answer questions in an informative manner.
GPT-2 is built upon the transformer architecture and is designed with 1.5
billion parameters. The model is adapted to perform a wide range of natural
language processing tasks without task-specific training, including
translation, summarization, and content generation Vaswani et al. (2017).

GPT-2 is an autoregressive language model. In the autoregressive approach, a
sequence of tokens (words or subwords) is generated one token at a time, with
each new token being conditioned on the previous ones. Mathematically, the
probability of a sequence $s=(t_{1},t_{2},\cdots,t_{N})$ is factorized
as:$P(s)=\prod_{i=1}^{N}P(t_{i}|t_{<i})$ Where $x_{<i}$ represents all the
tokens before the $i^{th}$ token. Each conditional probability
$P(t_{i}|t_{<t})$ is estimated using the transformer architecture, which
allows the model to capture long-range dependencies between tokens
effectively.

Assuming GPT-2’s transformer has $L$ layers and a hidden dimension $d$. The
model uses an input/output embedding matrix $E\in\mathbb{R}^{|V|\times d}$,
where V represents the vocabulary. For any token in the input sequence and at
any layer $l$, the corresponding output is $h^{l}_{i}$. The model predicts the
token $t_{i}$ by projecting its final hidden state $h^{L}_{i}$ onto the
embedding matrix. The final token prediction is then obtained by selecting the
token corresponding to the maximum value in this distribution, which can be
formulated as:

$\hat{t}_{l}=\mathrm{arg\,max}_{t\in\mathcal{V}}\ Eh_{i}^{L}$ (1)

By conditioning on prior tokens, GPT-2 can effectively generalize based on the
natural language biases it encountered during its training on vast amounts of
internet text. This training data, which inherently captures a snapshot of the
world’s information and perspectives at the time of collection, enables the
model to not only reproduce language patterns but also to discern syntactic
and semantic similarities. While its performance is commendable, GPT-2 also
sparked a discussion about ethical concerns in AI, given its ability to
generate coherent yet fabricated content. OpenAI initially withheld the model
from public release, citing these concerns, but later provided a staged
release as the community became familiarized with its potential and
limitations OpenAI (2019).

#### Stable Diffusion

Stable diffusion is a latent text-to-image diffusion model that was first
introduced in the paper "High-Resolution Image Synthesis with Latent Diffusion
Models" by Rombach et al. (2022). It works by gradually denoising a latent
representation of an image, while conditioning the process on a text prompt.
Stable diffusion models are trained on a large dataset of image-text pairs and
can generate high-quality images from a wide variety of text prompts. The
denoising process in stable diffusion can be formally described as follows:
Let $x_{t}$ be the latent representation of the image at time $t$, and let
$\mathcal{N}(\cdot)$ be a Gaussian noise function. The denoising process is
then given by the following equation:

$x_{t+1}=\alpha(x_{t})+\mathcal{N}(\sigma)$ (2)

where $\alpha$ is a denoising function, and $\sigma$ is the standard deviation
of the Gaussian noise. The denoising function $\alpha$ is a neural network
that is trained to remove noise from images. The denoising process is repeated
iteratively until the desired level of noise reduction is achieved. The
denoising process in stable diffusion is conditioned on a text prompt. This is
done by passing the text prompt to the denoising function $\alpha$,
implemented using cross-attention mechanisms Vaswani et al. (2017). This
conditioning process allows stable diffusion models to generate images that
are consistent with the text prompt. For example, if the text prompt is “a cat
sitting on a mat,” the denoising function will generate an image of a cat
sitting on a mat. One of the key advantages of stable diffusion models is that
they are very stable and easy to control. This makes them well-suited for a
variety of applications, such as generating realistic images for creative
projects, or creating synthetic images for training other machine learning
models.

###  3.3 Utilizing Data-Driven Bias in GenAI Models to Evaluate Copyright
Genericity

By detecting correlations and generalizing patterns from massive corpora of
preexisting expressive knowledge, GenAI models can offer a unique opportunity
to detect genericity and assess the originality of creative works. As humans,
we routinely engage with the corpus of preexisting materials, learning from
images, styles, themes, colors, compositions, and the like. Humans memorize
impressions, extract principles and generalize from new materials they
observe, deconstruct, and reconstruct. All these processes take place
exclusively in the silo of the human mind. In an analogy, GenAI also learns
from engagement with preexisting materials, but on a much grander scale. Its
capacity to learn from data at different levels of granularity reveals some
underlying shared patterns in preexisting works, which have been difficult to
accurately measure thus far. During learning, models conceptually identify and
prioritize expressive elements, as well as rank them according to their
frequency in the model’s dataset, which can potentially be harnessed to allow
assessing expressive genericity at scale. Generative models tend to produce
outputs that reflect the most common patterns found in their training data. If
a pattern is frequent (“generic”) in the dataset, the model is more likely to
reproduce it. On the other hand, less frequent (“original”) patterns are less
commonly produced Brown et al. (2020). This inherent "bias" stemming from data
is a core aspect of machine learning, influencing how GenAI models generalize
liu2022self. To illustrate this bias, we conduct the following experiment: we
train a stable diffusion model from scratch only on images featuring 2 and 10
circles with a white background. The model was trained on a synthetic dataset
of size 10K, and we generated 1K images (examples shown in Figure 1). We then
compare the distributions of circle occurrences in the training set vs the
generated set. As seen in Figure 2. The model was able to generalize and
generate images with unseen number of circles in the training set, however it
is biased toward generating images with the same number of circles as in the
original data. Similar experiments have been performed on past GenAI models
such as GANs and VAEs with similar results Liu et al. (2022).

Figure 1:  Distributions of circle occurrences in images using a stable
diffusion model: (Left) Training dataset, specifically images containing
either 2 or 10 circles; (Right) Distribution observed in the generated images.

The same dynamics may be demonstrated using an inpainting technique, which
requires GenAI models to reconstruct missing parts from images Rombach et al.
(2022). As shown in Figure 2, when we tasked the publicly available pretrained
Stable Diffusion with completing a photo of a bench that initially included a
dog, the model reconstructed the bench and removed the dog from the scene, as
can be expected. This behavior indicates the model has encountered more empty
benches in the training set than benches with dogs, and hence is biased
towards generating them. Similarly, in the artwork domain, when masking the
apple from René Magritte’s famous painting The Son of Man, the model
reconstructed the image with human male faces rather than with apples Carlini
et al. (2021).111the reason for this is that the model was trained
(unsurprisingly) on many more images of men’s faces rather than men with
apples in front of their faces.

Figure 2: an inpainting exercise to reconstruct the bench from a photo of a
dog on a bench (Top) and apple in René Magritte’s painting The Son of Man
(Bottom).

Lastly, in the context of LMs, we display this phenomenon on the publicly
available pretrained GPT2 model, by prompting prefix of idioms (all words
except for the last word), demonstrating the model’s natural bias. For
example, when prompting the model with the idiom “put words in someone’s,” it
completes it to “mouth” with a high probability of 0.74 as it was likely seen
in the training set frequently Haviv et al. (2023). On the other hand, when
prompting the model “play it by” instead of predicting “ear”, it predicts
another plausible continuation “yourself” with probability of 0.37. These
findings are consistent with copyright law. Generic benches and facial human
expressions are unprotected by copyright law. The same is true for idioms and
other common expressions CFR (2004); Per (1984). Thus, the positive
correlation between expressive genericity and the GenAI models’ likelihood of
recreation is illuminating to copyright law. Since the level of genericity
confines the legal protection that the law affords expressive works, 1 We
tried this exercise also with adding to the model the textual prompt “Magritte
the Son of Man”, but the outputs were still dominated by men’s faces.222This
outcome may also demonstrate that the model’s training data might be biased in
favor of men images wearing a suit and a hat, rather than images of women. But
it is difficult to tell. Given that the body shape is clearly masculine, and
so are other cues in the outfit, the model may properly recognize the
appropriate gender. Courts and policymakers could use this measurement for
delineate the scope of copyright protection by measuring the genericity of
expressive elements.

##  4 Discussion

The approach proposed by this paper illuminates some limitations in
contemporary research. For example, in some cases, researchers may wrongly
interpret the literature and equate data memorization in GenAI models with
copyright infringement Chang et al. (2023); Sag (2023); Bracha (2023). For
example, Carlini et al. (2023), and Somepalli et al. (2023) have used data-
extraction attacks to show that GenAI models can reconstruct specific
copyrighted works that appear in the GenAI models’ datasets. However, such a
conclusion is inaccurate from a legal standpoint. To the extent that the
models’ datasets reflect the cultural environment of a particular expressive
domain accurately, memorization is likely to be lawful, even desirable, from a
legal perspective Elkin-Koren et al. (2024). When the training models’
datasets are biased towards certain expressive elements, these elements are
likely to be considered “generic” and, accordingly, narrowly protected by
copyright law. Thus, memorization that is in line with the data-driven bias
does not indicate copyright infringement. To ensure that GenAI models
sufficiently represent the cultural state of expressive domains, model
developers might need to de-duplicate and curate their datasets from pirated
or otherwise unlawful material Kandpal et al. (2022); Lee et al. (2022). They
might also need to make sure that the dataset is comprehensive enough and
updated periodically to reflect the dynamic evolution of the changing
expressive discourse Lund (2009). Our approach also illuminates why any rigid
or binary solutions for ruling out copyright infringement are misguided. For
example, one line of research seeks to harness differential privacy or other
algorithmic stability approaches to “safeguard” generative models from
engaging in copyright infringement Vyas et al. (2023). These approaches limit
model production to outputs that are not over-influenced by any particular
copyrighted work within their data set. The intuition that guides these
approaches-allowing outputs to draw only on common expressive features that
are not original to any particular work—is correct. Nevertheless, algorithmic
stability approaches cannot serve as a definitive binary test for copyright
infringement Henderson et al. (2023); Elkin-Koren et al. (2024). For example,
a model that was trained on many derivatives of an original copyrighted work
may produce an infringing derivative that work even if the work itself did not
appear in the training dataset Grimmelmann (2023). The same criticism may be
extended to a broader body of research that proposes to employ output filters
to prevent copyright infringement Ziegler (2021). Other than being hackable
Ippolito et al. (2023); Henderson et al. (2023), the main problem with filters
is that they must boil down flexible legal standards (in our case: substantial
similarity of original expression) into rigid rules Kaplow (1992). However, as
a rich body of scholarship in the context of copyright Bartholomew (2014);
Boroughf (2015) and elsewhere Dwork et al. (2012); Kairouz et al. (2021) has
shown, this approach is inadvisable as it may lead to systematic over or
under-enforcement. For this reason, in lieu of rigid binary standards, this
paper proposes to harness GenAI models to indicate the extent to which an
output is already generic, and, therefore, worthy of weaker protection. Using
data-driven bias as a metric for (lack of) originality will not provide a
definitive answer as to whether GenAI models’ outputs infringe the copyrights
of works used in their training. Nevertheless, genericity measurements would
be a valuable input for courts and policymakers, and inform their legal
analysis when grappling with these issues. Specifically, armed with the
ability to identify generic patterns in copyrighted works, courts and
litigants could better reflect on the sakes of the litigated conflicts, rule
in a more predictable manner in tough cases, and avoid unnecessary litigation
in easy ones. In addition, the Copyright Office could substitute its current
rigid guidelines which denies copyright registration (and protection) for all
GenAI works Federal Register (2023),with a more nuanced standard that
accommodate registration (and protection) for highly original works Miller
(2009); Parchomovsky and Stein (2009).

##  5 Conclusions

This paper has introduced a novel approach that leverages GenAI data-driven
biases to measure genericity in copyrighted works on a large scale. Our
approach may introduce more nuance to copyright analysis, thereby avoiding
over protection of elements that should remain in the public domain to better
serve the objectives of copyright law. This approach may inform legal
practitioners and copyright stakeholders during infringement litigation, and
rights’ registration or licensing. Our approach does not advocate for
technological solutionism or full automation of the copyright system; rather,
it aims to provide evidence that can inform normative tradeoffs made by social
institutions. “Genericity scores,” as proposed in this article, have the
potential to empower policymakers in devising more effective copyright
policies and doctrines that align with the evolving landscape of "cheap
creativity" facilitated by GenAI. Ultimately, our method can offer valuable
insights to policymakers as they navigate the challenges of adapting copyright
law to the digital age.

#### Acknowledgments

This research was funded in part by an ERC grant (FOG, 101116258), as well as
an ISF Grant (2188 $\backslash$ 20). Views and opinions expressed are however
those of the author(s) only and do not necessarily reflect those of the
European Union or the European Research Council Executive Agency. Neither the
European Union nor the granting authority can be held responsible for them. In
addition, the research leading to these results was supported by TILabs Tel-
Aviv University Innovation Labs.

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