# Strategic Behavior and AI Training Data††thanks: Email:
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>The first author led the
project and contributed the most, while other authors who made significant
contributions are listed alphabetically. We thank Anthi Kiouka for valuable
input and discussions. We also thank Timothy Carbone at Unsplash for
generously answering our questions, giving helpful insights and providing
access to data. We are grateful to Heski Bar-Isaac, Stefan Bechtold, Chiara
Farronato, Christophe Gösken, Brad Greenwood, Ginger Zhe Jin, Reinhold Kesler,
Sijie Lin, Ananya Sen, Holger Spamann, Catherine Tucker and Joel Waldfogel for
helpful comments. The paper has benefited from the feedback of participants at
seminars and conferences at Technis Webinar, ETH Zurich (Center for Law and
Economics), Harvard Business School (Digital Competition and Tech Regulation
Conference), and NYU Stern School of Business.

Christian Peukert111Corresponding author. Florian Abeillon  Jérémie Haese
Franziska Kaiser  Alexander Staub

(April 2024)

###### Abstract

Human-created works represent critical data inputs to artificial intelligence
(AI). Strategic behavior can play a major role for AI training datasets, be it
in limiting access to existing works or in deciding which types of new works
to create or whether to create new works at all. We examine creators’
behavioral change when their works become training data for AI. Specifically,
we focus on contributors on Unsplash, a popular stock image platform with
about 6 million high-quality photos and illustrations. In the summer of 2020,
Unsplash launched an AI research program by releasing a dataset of 25,000
images for commercial use. We study contributors’ reactions, comparing
contributors whose works were included in this dataset to contributors whose
works were not included. Our results suggest that treated contributors left
the platform at a higher-than-usual rate and substantially slowed down the
rate of new uploads. Professional and more successful photographers react
stronger than amateurs and less successful photographers. We also show that
affected users changed the variety and novelty of contributions to the
platform, with long-run implications for the stock of works potentially
available for AI training. Taken together, our findings highlight the trade-
off between interests of rightsholders and promoting innovation at the
technological frontier. We discuss implications for copyright and AI policy.

Keywords: Generative Artificial Intelligence, Training Data, Licensing,
Copyright, Natural Experiment

##  1 Introduction

Data is a key input to artificial intelligence (AI) that enables societal and
economic progress through the discovery of knowledge, enhancement of
productivity, and expansion into new or existing markets (Wu et al., 2020;
Brynjolfsson et al., 2023; Beraja et al., 2023; McElheran et al., 2024). The
rapid proliferation of generative AI (genAI) technologies has enabled hundreds
of millions of users to generate high-quality text (including software code),
images, audio, and video at a negligible cost. All this is made possible by
the vast stock of data available on the public internet, which together with
computing power, is responsible for the tremendous performance improvements in
recent large language models (Ho et al., 2024; Metz et al., 2024). However,
access to a continuous flow of data remains crucial for the performance of a
wide range of AI applications in dynamic social environments (Hadsell et al.,
2020; He et al., 2011; Peukert et al., 2023; Valavi et al., 2022; Whang et
al., 2023). Input data for genAI models, in particular, comes mostly from
online content in the form of text, images, sound, and video produced by
humans. Hence, human behavior and incentives for individuals and organizations
to contribute to training datasets play a major role for the quality of genAI
(Bauer et al., 2016; Sokol and Van Alstyne, 2021; Burtch et al., 2024; del
Rio-Chanona et al., 2023; Quinn and Gutt, 2023).

Questions of whether, how, and which data can or should be utilized to train
AI have moved to the center of the policy debate. Regulatory frameworks – or
the lack thereof – can significantly impact both the demand for and supply of
data. With respect to input data, AI policy intersects with privacy law and
competition policy (Jones and Tonetti, 2020; Farboodi et al., 2019). More
recently, however, intellectual property law has emerged as a critical area of
the policy discourse because input data may be subject to copyright protection
(Eshraghian, 2020; Samuelson, 2023). While some jurisdictions allow exemptions
for research and development in copyright law, there is general legal
uncertainty (Fiil-Flynn et al., 2022; Henderson et al., 2023). Several high-
profile lawsuits against AI developers allege, among other claims, direct
copyright infringement by creating unauthorized copies of their works and
using such copies as training data.222See articles in the New York Times
(http://tiny.cc/pxljxz) and Reuters (http://tiny.cc/txljxz). As a result,
policymakers around the world are tasked with balancing innovation in AI and
the interests of rightsholders. So far, however, there is not sufficient
empirical evidence to guide policy (Peukert and Windisch, 2024).

In this paper, we shine a light on how strategic behavior shapes the flow of
data, studying the response of individual creators to their works being made
available for the training of commercial AI. Our empirical setting is one of
the largest stock photography websites in the world, Unsplash. In the summer
of 2020, Unsplash released metadata of a subset of 25,000 photos explicitly
for the training of commercial AI applications (hereafter the “LITE” dataset).
This natural experiment allows us to identify the causal effects of being
included in AI training data and characterize strategic responses of those
affected. We compare the upload behavior of contributors whose works were
included in this dataset to contributors whose works were not included. In
addition, we investigate changes in the variety and novelty of user
contributions due to the release of the LITE dataset. We measure variety and
novelty by calculating the similarity of each new upload to all existing
images one year prior to the treatment based on their associated keywords
using natural language processing methods. This allows us to track how variety
and novelty change over time for both treated and control users, before and
after the release of the LITE dataset.

We find that treated users left the platform at a higher-than-usual rate.
Conditional on remaining active, treated users substantially slowed down the
rate of new uploads by about 40% per month.

Our content analysis shows that within-users, uploads decrease in variety but
not in novelty compared to the existing stock of images. Across users,
however, the variety of uploaded images decreased by about 5% compared to the
existing stock and uploaded images were about 30% less novel. This implies
changes to the aggregated training dataset stemming mostly from changes in
user composition rather than from a shift in individual behavior. We show that
users with professional gear reduce their contributions significantly more, as
do more successful users. However, our results do not imply that photographers
leave the market entirely. We show that users’ behavioral changes only occur
on Unsplash and are not mirrored in their activity on Instagram, the most
popular image-sharing platform. Finally, we provide some evidence that the
behavioral response is stronger, the more AI may be perceived as an economic
threat. First, we show that the effect intensifies later when more capable
genAI models become widely known to the public. Second, users who have
multiple images in the training dataset reduce their activity almost twice as
much compared to users who had only one image included.

Overall, we document that strategic behavior affects the size as well as
aspects of the quality of the flow of data. A back-of-the-envelope calculation
suggests that making the entire catalog available for commercial AI research
(which is similar to a policy that would allow fair use of any copyrighted
material) would have reduced the flow of data by half. At the same time, the
flow would become increasingly similar to the stock, with the number of very
similar images tripling within a year.

Our contribution is twofold. First, we provide the first large-scale empirical
evidence on strategic behavior in the supply of data. This adds to a growing
literature that discusses the effects of AI on workers and platform
contributors (Acemoglu et al., 2022; Doshi and Hauser, 2023; Eloundou et al.,
2023; Felten et al., 2023; Huang et al., 2023; Quinn and Gutt, 2023). In
particular, we highlight upstream consequences of the proliferation of genAI
models, which is an under-explored topic (Lin, 2024). Second, our setting
allows us to estimate the causal effects of adding human-made works to
training datasets that can be used for the development of commercial AI
applications. Thereby we are able to provide important empirical evidence to a
primarily theoretical debate on policy-making in the age of AI (Fiil-Flynn et
al., 2022; Samuelson, 2023; Henderson et al., 2023; Gans, 2024; Yang and
Zhang, 2024; Wang et al., 2024).

##  2 Background and related literature

Regulation can affect the demand for data by defining rules about which data
can or should be used for AI applications. For example, privacy law and
competition policy can reduce firms’ abilities to track consumer behavior
(Johnson, 2022), require firms to elicit consent from consumers (Godinho de
Matos and Adjerid, 2022) or share data with competitors (Prüfer and
Schottmüller, 2021; Martens et al., 2024). Access to data has direct
consequences for the output of AI applications. The value of data, for example
for prediction exercises, varies with its quality and quantity (Lei et al.,
2023; Neumann et al., 2019; Peukert et al., 2023; Sun et al., 2023).

Concretely, the new AI Act of the European Union will demand that “Training,
validation and testing datasets shall be relevant, sufficiently
representative, and to the best extent possible, free of errors and complete
in view of the intended purpose. They shall have the appropriate statistical
properties, including, where applicable, as regards the persons or groups of
persons in relation to whom the high-risk AI system is intended to be
used”.333See Article 10 of the Proposal of the European Parliament and of the
Council on the Artificial Intelligence Act from January 2024,
https://data.consilium.europa.eu/doc/document/ST-5662-2024-INIT/en/pdf.
However, such provisions also have intersections and perhaps even
contradictions with intellectual property law. In the EU, for example,
although copyright law was only recently modernized with the Directive on
Copyright in the Digital Single Market in 2019, discussions about further
reform are in full swing as part of legislation on the regulation of AI. When
rightsholders can exercise exclusive rights and restrict access to certain
source materials, copyright may, in turn, create or promote biased AI systems
(Levendowski, 2018).

On the other hand, the supply of data is discussed less. With the abundance of
data online, researchers and AI developers can potentially access a large
stock of data, most of which was created before the dawn of AI. In general,
surprisingly little is known about input datasets for AI applications.
Interesting empirical work comes from the computer science community. Birhane
and Prabhu (2021) study a widely-used training dataset of images concerning
privacy issues (the presence of human faces), sexually explicit content, and
measures of the age and gender distribution of depicted humans. In related
recent work, Guilbeault et al. (2024) study gender bias in about one million
images downloaded from the web. In many applications, access to a large stock
of historical data is sufficient. Yet, in dynamic settings, where the stock of
data can become outdated, a consistent flow of data is key for algorithmic
performance (Hadsell et al., 2020; He et al., 2011; Valavi et al., 2022;
Peukert et al., 2023; Whang et al., 2023). However, the flow of data is
endogenously determined by strategic behavior, which is, in turn, influenced
by institutional settings such as privacy law, competition policy, and
intellectual property law.

Key criteria that define the quality of training datasets concern the variety
and novelty of information in the dataset. To illustrate this, consider two
examples. First, imagine a panel dataset that an econometrician would analyze.
We can increase variety by adding more cross-sections, e.g. more firms, and we
can increase novelty by expanding the time series, i.e. adding more years per
firm. As a second example, consider a dataset with photos of New York City. We
can add to the variety dimension by adding images of buildings from every
neighborhood, from different angles, at different times of the day, etc.
However, as some buildings make way for new developments, we need to add novel
images of the same concept, e.g. the skyline, to ensure a continued high-
quality dataset. Variety and novelty can be measured by changes coming in the
flow of incoming data points and their relation to the existing stock of data.
As a result, strategic behavior that alters the flow of data can also alter
key dimensions of dataset quality and, therefore, the output quality of AI
applications.

Recent theoretical work is useful as a starting point to think about policy
options to solve the trade-off between copyright protection and AI innovation
(Gans, 2024; Yang and Zhang, 2024). Digital goods, such as user-generated
content or creative works, tend to have high fixed costs and near-zero costs
of copying. Intellectual property rights or alternative business models create
rents that allow creators to recoup their fixed-cost investments. The
proliferation of genAI systems suggests two potential outcomes: AI might
reduce these monopoly rents, leading humans to produce only works with lower
fixed costs, or AI might increase human productivity, effectively reducing
fixed costs and requiring lower monopoly rents to incentivize creation. These
effects can depend on factors such as the bargaining power of rights holders
(Gans, 2024) and the existing body of work in a particular domain (Yang and
Zhang, 2024). A potential solution is a licensing mechanism that compensates
rightsholders with a royalty scheme that is determined by Shapley values of
pieces of training data that end up in the final outcome (Wang et al., 2024).

Strategic behavior of contributors to training datasets, especially when large
parts of the public internet can be considered as a huge training dataset, can
have intricate long-run effects on online communities. Evidence from platforms
like Stack Overflow suggests that the introduction of genAI systems like
ChatGPT can lead to reduced community participation, a decrease in the number
of questions and users as well as a decline in the quantity and quality of
answers (Burtch et al., 2024; Quinn and Gutt, 2023; Gallea, 2023), and an
overall decrease in activity (del Rio-Chanona et al., 2023). Three recent
working papers are most directly related to our paper. Quinn and Gutt (2023)
discuss how the quality of training data can change because of changes in user
behavior. In the context of Stack Overflow, they show that changed user
behavior affects the underlying quality of the training data for the next
iterations of AI models. Huang et al. (2023) investigate the impact of genAI
on content creators across two major Asian platforms, Lofter and Graffiti
Kingdom, through quasi-experiments. The study reveals that introducing genAI
tools on Lofter decreased creator activity, whereas banning these tools on
Graffiti Kingdom had a mixed impact: Non-churning creators increased their
activity, while that of individuals with copyright concerns decreased.
Similarly, Lin (2024) studies the online art platform DeviantArt and shows
that contributors decreased their publication volume by 22% after the platform
introduced a genAI feature that let users generate art with a click of a
button. In summary, while evidence on the impact of genAI trained on vast and
intransparent datasets is growing, the lack of research on contributors’
responses to the explicit use of their work for the development of commercial
AI models forms the backdrop of our study.

##  3 Empirical setting

###  3.1 Unsplash

Unsplash is a stock photo platform hosting approximately 6 million photos and
illustrations, submitted by roughly 360,000 contributors. According to
Similarweb, Unsplash is among the top 5 most visited websites in the
“photography” category globally, receiving about 30 million visits each month.
Over the past decade, Unsplash has recorded a cumulative total of 1 trillion
image views, coming from tens of millions of individual users and thousands of
API partners.

Unlike other stock photography websites, the vast majority of images on
Unsplash are made available under a permissive license, which allows all
images on the platform to be used freely for both non-commercial and
commercial purposes. Users can, of course, revoke this license by deleting
individual images or their entire accounts at any time. Unlike other websites
that make images available under similar licenses (e.g. Wikimedia),
contributions on Unsplash are of a high quality. Partially, this is enabled by
rigorous moderation efforts of an editorial team that hand-selects images that
are prominently shown on the website. As of May 2023, the curation team has
selected about 300,000 images for curation, which represents about 6% of all
images available on the platform.

In October 2022, Unsplash launched a paid subscription service called
Unsplash+. Among other things, after being accepted into the program as a
contributor, users can receive monetary compensation for uses of the
works.444See https://unsplash.com/blog/contribute-to-unsplash/. Further, the
Unplash+ license explicitly disallows the use of images for machine learning,
AI, and biometric tracking technology.555See
https://unsplash.com/plus/license.

###  3.2 The Unsplash research program

####  3.2.1 Two-tiered access to training data for AI

In August 2020, Unsplash released a dataset containing 25,000 “nature-themed”
and “featured” photos available for commercial and non-commercial use,
including for the training of AI. This dataset is called the LITE dataset. In
addition, the platform released a dataset covering the whole collection of
images hosted on the platform (hereafter the FULL dataset) made available
exclusively for non-commercial research purposes. Access to the LITE dataset
happens with the click of a button, access to the FULL dataset is subject to
an application process with a thorough manual review process. The datasets do
not contain the images per se (they can still be downloaded quite effortlessly
through an API), but costly-to-compute metadata such as keyword tags, depicted
landmarks, color distributions, etc. By releasing the two flavors of the
dataset, Unsplash essentially treated their entire community with their image
(metadata) being available for AI research. However, the images that were
selected for the LITE dataset receive a much stronger treatment because access
is quick, free, and subject to less restrictive usage permissions.

####  3.2.2 The selection of images in LITE

The LITE dataset was created on June 25, 2020, and, therefore, is a subset of
all images available on Unsplash up until that date. The images to be included
in the dataset were selected using the following database query:666See
https://github.com/unsplash/datasets/issues/55 for details on the sampling
procedure as expressed by Timothy Carbone, Head of Data at Unsplash, who runs
the research dataset program. In an email conversation with us, he shared the
database query he used to select the images in LITE and clarified further:
“The Lite dataset’s only objective is to give a preview of the content that’s
in the Full dataset. Here are the motivations behind its content: (1) Focusing
on a single keyword helps the end-user accessing more depth of content, rather
than breadth. This is allegedly more helpful for things like training AI and
will hopefully provide a better ‘trial’ experience for the dataset. (2) Making
sure we only choose curated content helps with lowering the risk of the
content being removed from the platform and makes the dataset easier on the
eye.”

⬇

1SELECT DISTINCT photos.id FROM photos

2JOIN tags ON tags.photo_id = photos.id

3WHERE tags.keyword = ’nature’ \-- The photo is tagged with "nature"

4 AND tags.confidence > 0.9 \-- We are more than 90% confident that "nature"
is present in that photo

5 AND photos.curated = TRUE \-- photo has been curated by the editorial team

6 AND photos.status = 1 \-- photo is still available on the platform

7LIMIT 25000

Unsplash uses third-party computer vision algorithms to automatically add tags
to images, most prominently Amazon’s Rekognition service.777See
https://unsplash.com/blog/the-data-stack-at-unsplash/ and
https://docs.aws.amazon.com/rekognition/latest/APIReference/API_DetectLabels.html
for details. These auto-generated keywords come with a confidence score which
ranges from 0 to 1.

Curated images are photos that were chosen by a human curator at Unsplash to
be “special”, i.e., unique perspective, exploration of light and materials,
creative use of color, inventive framing, evoking a mood, usefulness for
reuse. These photos were either featured on the front page of the website or
featured for ten days under a specific topic (e.g.: “nature”).888See
https://unsplash.com/blog/how-we-choose-what-photos-to-feature-on-the-
unsplash-homepage/.

The fact that there was no specific ordering in the SQL query, i.e. no ORDER
BY clause, leads to an unpredictable row order of the results. The row order
depends on the database’s internal storage and retrieval mechanisms, which can
be influenced by factors like the structure of the database’s indexes and how
the query optimizer chooses to execute the query.999The documentation of
PostgreSQL, which is the database system Unsplash uses, states: “When using
LIMIT, it is a good idea to use an ORDER BY clause that constrains the result
rows into a unique order. Otherwise you will get an unpredictable subset of
the query’s rows […] You don’t know what ordering unless you specify ORDER BY.
[…] If ORDER BY is not given, the rows are returned in whatever order the
system finds fastest to produce.”, see
https://www.postgresql.org/docs/current/sql-select.html and “If sorting is not
chosen, the rows will be returned in an unspecified order. The actual order in
that case will depend on the scan and join plan types and the order on disk,
but it must not be relied on. A particular output ordering can only be
guaranteed if the sort step is explicitly chosen.”, see
https://www.postgresql.org/docs/current/queries-order.html.

Table 1: Sort order in LITE dataset

| (1) | (2)
---|---|---
AutoKeywordScore | -3.3952∗∗∗ | -3.4890∗∗∗
| (1.1170) | (1.1132)
ImagePopularity | -0.0000 | -0.0000
| (0.0000) | (0.0000)
UserKeyword | 136.1898 | 142.9112
| (119.9305) | (120.5190)
ImageAge | -25.7948 | 13.3226
| (37.7172) | (48.3746)
UserPopularity |  | 0.0545
|  | (0.0508)
AccountAge |  | -53.3435
|  | (43.0708)
adj. R2 | 0.0003 | 0.0003
Observations | 25,000 | 24,964

Note: White-robust standard errors in parentheses. ∗ $p<0.10$, ∗∗ $p<0.05$ ∗∗∗
$p<0.01$

Table 1 shows that the order of images in the LITE dataset cannot be explained
by image characteristics such as image age (i.e. time since upload), image
popularity (i.e. views and downloads) or whether the user has chosen the
keyword “nature” to describe the image. Also, user characteristics such as the
total sum of views and downloads of the users’ images (UserPopularity) and the
time since the first upload of the user (AccountAge), do not explain the order
of images in the LITE dataset.101010Note that we do not have user-level
information about 14 users, because their accounts were deleted before we can
observe them in FULL 1.0.1, the earliest FULL dataset we have access to. These
users have 63 images in the LITE dataset. Hence, the number of observations in
column 2 is smaller than in column 1. Only the confidence score associated
with the automatically assigned keyword “nature” is correlated with the sort
order. The higher the confidence score of an image, the higher up it appears
on the list. This is consistent with PostgreSQL using a descending order to
generate a list of all keywords that meet the confidence condition. For
example, conditional on meeting the threshold, keywords with a confidence
score of 0.99 would appear before keywords with a confidence score of 0.90.
However, it is worth stressing that the regression can only explain little
variation in the rank order, as shown by the low value of the adjusted
$R^{2}$.

With this information, we can construct a control group of images, for
example, consisting of those images that would have shown up below row 25,000
had the query not used a LIMIT condition. Based on the results in Table 1,
images would be listed further up, the higher the confidence value of the
computer vision algorithm for the keyword “nature”. For technical reasons,
however, this does not necessarily mean that those images monotonically convey
more of the concept “nature”. They can also convey the same amount of “nature”
but less of other concepts. In multi-label classification algorithms using
Deep Learning and Convolutional Neural Networks, as deployed in the
DetectLabels service of Amazon Rekognition, the calculation of confidence
scores is a nuanced process.111111The exact technical underpinnings of
Rekognition are not publicly disclosed, but a slide deck from a System Dev
Engineer at Amazon Webservices gives some indication about the service’s
architecture, see https://iptc.org/download/events/phmdc2017/IPTC-
PhMdC2017-AHornsby-AmazonRekognition.pdf. The last layer typically applies the
softmax function, transforming logits – the network’s final layer output,
representing unnormalized log probabilities – into a probability distribution
across the predicted labels. Softmax is non-linear implying that changes in
logits can be disproportionally reflected in confidence scores. Moreover, the
confidence score for a given label is not only a function of its own logit but
also depends on the logits of all other labels. Therefore, an increase in the
confidence score of “nature”, disproportionally so at the high end of the
range, may result from a larger logit of the concept itself or from smaller
logits of other concepts.

####  3.2.3 Announcement and stakeholders’ awareness of the program

The release of the research datasets was announced publicly on the Unsplash
blog on August 6, 2020.121212See https://unsplash.com/blog/the-unsplash-
dataset/. In this announcement, Unsplash mentioned the target users for the
dataset: “We’re releasing the most complete high-quality open image dataset
ever, free for anyone to use to further research in machine learning, image
quality, search engines, and more.” They also describe the differences between
the two versions: “We’re releasing the data in two versions: a LITE dataset
available for commercial and noncommercial usage, and the FULL dataset
available for noncommercial usage.” Importantly, they also acknowledge that
the dataset is not a one-shot project: “As the Unsplash library continues to
double in size every year, we’ll continue updating the dataset with new fields
and new images.” Every user who subscribes to the Unsplash newsletter, which
is the default option when signing up for an account, receives periodic emails
summarizing announcements on the Unsplash blog. Therefore, we have strong
reasons to believe that the average user was aware of the research dataset
program. Users were not able to opt out, but were, of course, free to delete
affected images or their user accounts. Users also did not receive monetary or
other compensation for having images included in the LITE nor the FULL
dataset. The release of the Unsplash dataset was well received by the AI
community. The GitHub repository, which provides documentation and sample
code, was starred 2,300 times, and there is an active discussion with the
community via issues and pull requests.131313See
https://github.com/unsplash/datasets. In general, according to PapersWithCode,
data from Unsplash has been used in at least 24 computer science papers for
problems such as Image Augmentation, Image Generation, Image Inpainting and
Outpainting, and Text-to-Image Generation.141414See
https://paperswithcode.com/datasets?q=unsplash&v=lst&o=match. Altogether,
these are strong reasons to believe that the release of the LITE dataset was
sufficiently salient to Unsplash users to allow us to detect treatment
effects.

##  4 Data and methods

###  4.1 Available information

We have access to rich metadata on more than 4.9 million images, which
represents all images uploaded to Unsplash since May 2013 and still available
in June 2020 and/or in May 2023 (FULL dataset). In particular, we combine
information from datasets released in August 2020 (but created on June 25,
2020) and in May 2023.151515To be precise, we have access to datasets in
version 1.0.0 (LITE), and versions 1.1.0 and 1.2.1 (FULL). See
https://github.com/unsplash/datasets/blob/master/CHANGELOG.md. We observe the
user account that has uploaded the image, the upload date, whether an image
was curated, a list of keywords associated with each image, and metadata on
camera gear for photos. We also observe whether a user has chosen to display a
badge on their Unsplash page that says “Available for hire” and for a subset
of users (those who chose to reveal this information in their Unsplash
profile), we also know their Instagram account name and have collected
information on their upload history on Instagram.161616This information is not
available in the LITE or FULL datasets. We, therefore, do not have time-
varying information, but we were able to get a snapshot on February 5, 2024,
from which we observe the for-hire status and Instagram information as of that
day of about 11,352 out of 12,052 users from our preferred sample of treated
and control users. We observe Instagram activity for a subset of 1,084 users.
Not all Unsplash users reveal their Instagram accounts on their Unsplash
profiles. Further, technical restrictions require us to limit ourselves to the
20 most recent posts of a user. We exclude users for whom we only observe
posts in the after period as we do not know whether this is because the
account didn’t exist in the before period, whether there were no posts in the
before period, or whether the user is so active that we can only see posts
from the after period in our snapshot of the 20 most recent posts. As a
result, our sample is likely biased towards less active users with relatively
old Instagram accounts. Further, we know whether a user has joined the
Unsplash+ program as a contributor.171717We do not know when a particular user
joined the Unsplash+ program. However, we have this information as of April
23, 2024, approximately 2.5 years after the program’s launch.

Using information about the camera gear that the photographer used, we flag
users as professionals if at least one of the following conditions is
fulfilled for any photo they uploaded before the release of the LITE dataset.
First, the exposure time is longer than 5 seconds, which cannot be executed by
low-end cameras – including most smartphones – and without a tripod. Second, a
photo was shot with a focal length of more than 250, which typically requires
expensive professional lenses. Third, a photo is shot on a camera from a mid-
to-high-end brand, i.e., not on a smartphone.181818We do this based on the
following list of brands: Hasselblad, Leica, Rollei, RED, Blackmagic,
Voigtlander, Canon, Nikon, Fuji, Sigma, Olympus, Ricoh, Minolta, Konica,
Yashica, Kodak, Agfa, Noritsu, Epson, DJI.

###  4.2 Treatment and control groups

We exploit the fact that Unsplash released a subset of their entire platform
contents as a freely available LITE dataset. Because we want to study the
behavioral responses of users when their work is made available as a training
dataset for AI research, our treatment group is comprised of all 8,298 user
accounts that have at least one photo in the LITE dataset.191919Users can
change account names on Unsplash. Using the fact that the IDs of photos remain
unique and cannot be changed, we can track the same user even if they changed
account names. There are several ways to construct a control group. First, we
could simply take all users who had uploaded at least one image before the
LITE dataset was created but did not have any images included in the LITE
dataset. Second, we could take the set of users whose images fulfilled the
opposite of the selection criteria of the original database query discussed in
section 3.2.2. That is, we take users who had not uploaded nature-themed and
curated photos at the time when the LITE dataset was created. Third, we could
take all users whose images fulfilled the same criteria as in the original
database query but were not included in the LITE dataset. This approach comes
closest to resembling the perfect counterfactual for users in the LITE
dataset, making it our preferred specification. This specification of the
control group includes 3,754 users. In the robustness section, we also report
results from the two former approaches, showing that the results are
consistent, but our preferred approach yields the most conservative estimates.

###  4.3 Panel construction

To analyze dynamics over time, we need to create a panel structure at the user
level. We construct two separate panels. First, while we have information on
the upload date of every image, we do not have high-frequency information on
whether an image is still available on the platform. We have two snapshots
from which we can construct a dataset that tells us whether a user account we
observe carrying active images in June 2020 still has any available images in
May 2023.

Second, to study contribution dynamics in detail, we aggregate the data at the
monthly level and count the number of uploads of every user within that time
period. To create a balanced panel, we impute zeroes in months where we do not
observe any activity.

###  4.4 Analysis of variety and novelty

To investigate whether contributions to Unsplash change after the release of
the LITE dataset, we compare the flow of new uploads to the stock of images
one year before the launch of the research program. In particular, to capture
variety as a quality measure of a training dataset, we measure how similar
each new upload is relative to all existing images. To capture novelty, we
count the number of images in the stock of data that are very similar to a
focal new upload.

We then compare how variety and novelty evolve over time for treated users and
control users, before and after the release of the LITE dataset. We do so
based on the keywords that users assign to their uploaded images. The average
image has 10.28 keywords. We use natural language processing techniques to
assess the similarity between images based on their associated keywords in 4
steps.

  1. 1.

Aggregate keywords for each image and train a Word2Vec model on these
aggregated keywords to generate a vector representation for each unique
keyword. Herein, the Word2Vec model is configured to create 100-dimensional
vectors for each keyword, with a context window of 5 words and a minimum word
frequency threshold of 1, ensuring that even rare keywords are included in the
model.

  2. 2.

Apply the Word2Vec model to each image, converting the keywords into vectors,
and then calculate the average vector to represent the image’s semantic
content.

  3. 3.

Calculate cosine similarity scores between the vector representation of a
particular image and the vector representations of all existing images.

  4. 4.

Compute average similarity across all images and count the number of images
that exceed a specified similarity threshold, respectively.

We perform steps 1 and 2 for the stock of images uploaded by users that
eventually will be treated, as well as for the stock of images uploaded by
users in the control group. We define the stock as all images uploaded before
June 25, 2019. We perform steps 1-4 for all images uploaded by treated and
control users after June 25, 2019. This allows us to compare new uploads
starting one year before the start of Unsplash’s research program to new
uploads after the release of the first LITE dataset, across users in the
treatment and in the control group. We report 0.8 and 0.9 as the threshold
level for very similar images in step 4, where cosine similarity scores are
defined on the interval $[-1,1]$, with higher values corresponding to higher
similarity between images.

###  4.5 Identification strategy and econometric model

Our causal identification strategy is based on the notion that the images in
the LITE dataset are randomly drawn from a superset of all images on the
platform that meet the eligibility criteria. This implies quasi-randomization
at the user level. For our econometric analysis, we rely on a difference-in-
differences approach. We perform a set of OLS regressions saturated with fixed
effects to compare users with images in the LITE dataset to users who had at
least one image deemed eligible to be included in the LITE dataset but were
not included in the LITE dataset. Our baseline specification is

$\displaystyle Y_{it}$ $\displaystyle=\delta(Post_{t}\times
Treated_{i})+\eta_{t}+\mu_{i}+\varepsilon_{it},$ (1)

where $Y_{it}$ is the outcome of interest, e.g. the number of images uploaded
by user $i$ in month $t$. $Treated_{i}$ indicates whether user $i$ was
included in the LITE dataset. $Post_{t}$ is a binary variable that takes the
value 1 if the current month is after the release of the LITE dataset, and 0
otherwise. We include month fixed effects $\eta_{t}$ in all specifications,
and in some specifications, we additionally include user fixed effects
$\mu_{i}$. Because inclusion in the LITE dataset is the result of a natural
experiment with randomization, the parameter $\delta$ estimates the causal
effect of being included in a dataset made available for AI training on user
behavior.

##  5 Results

We begin by reporting baseline results separately for users’ deletion rates as
well as their uploading behavior. Then, we discuss effect heterogeneity by
different types of users and different types of images.

###  5.1 Baseline results

####  5.1.1 Changes in deletion rates

First, we study whether being included in the LITE dataset affects the
likelihood of an image or a user account being deleted from the platform.
Hence, this set of results differs from all other analyses else below, which
focus on users’ uploading behavior.

It is important to note that while we know the exact upload date of each
image, we do not know the exact deletion dates of either images or user
accounts. However, we can use the fact that Unsplash periodically releases
updates of the FULL dataset, which encompasses the entire platform catalog at
a particular point in time. We can trace images (and corresponding users) that
were part of the LITE dataset (created in June 2020) and observe whether they
were still available on the platform in May 2023.

Table 2: Results: Likelihood of remaining on the platform

| Images | Users
---|---|---
| (1) | (2) | (3) | (4) | (5)
Treated | 0.0220∗∗ | 0.0219∗∗ | 0.0109∗∗∗ | -0.0075∗ | -0.0084∗∗
| (0.00875) | (0.00879) | (0.00155) | (0.00395) | (0.00406)
Mean Control | 0.9075 | 0.9075 | 0.9075 | 0.9624 | 0.9625
Image-Age FE | Yes | Yes | Yes | Yes | Yes
User FE | No | No | Yes | No | No
Observations | 705,268 | 704,848 | 704,848 | 12,033 | 11,607

Note: We estimate a linear probability model. The dependent variable is an
indicator of whether an image or a user account is still available on Unsplash
in May 2023. Treated indicates whether a user was part of the LITE dataset. We
compare the survival of (images by) users who were part of the LITE dataset to
the survival of (images by) users who were not part of LITE dataset over a
period of 2.5 years (from Aug 20 to May 23). Fixed effects for the image’s age
(in months). Standard errors clustered at the user level in parentheses. ∗
$p<0.10$, ∗∗ $p<0.05$ ∗∗∗ $p<0.01$

In column (1) of Table 2, we see that images that were included in the LITE
dataset (i.e. that were treated) have a 2 percentage points higher chance of
surviving on the platform than images that were not included in the LITE
dataset. In column (2), we limit the sample to images of photographers that
have at least two uploads and find the same result. We do so to compare
against the specification in column (3), which has user-fixed effects. The
coefficient there implies a 1 percentage point higher likelihood of survival.
This suggests substantial differences in the variation between users and
within users. Overall the results suggest that treated images are deleted at a
rate of 7-8% ($\approx$ 3% per year), whereas control images are deleted at a
rate of 9% ($\approx$ 4% per year). In columns (4) and (5), we turn to user
accounts. We see that treated user accounts are less likely to survive than
accounts of control users. The estimate is about 1 percentage point in both
samples of all users and those users with at least two observations (i.e., the
users in the sample in columns 2 and 3). This implies that accounts of treated
users are deleted at a rate of 3% ($\approx$ 1% per year), whereas control
users delete their accounts at a rate of 4% ($\approx$ 1.5% per year).

####  5.1.2 Changes in users’ uploading behavior

Figure 1: Number of uploads, treatment versus control group

Panel A: Levels

Panel B: Differences

Note: In Panel A, we depict the average number of uploaded images for users in
the treatment group (solid) and users in the control group (dashed). In Panel
B, we plot OLS estimates of the $\delta_{\tau}$ coefficients are obtained from
$Y_{it}=\sum_{\tau\in T}\nu_{\tau}+\sum_{\tau\in
T}\delta_{\tau}\left(\gamma_{\tau}\times
Treated_{i}\right)+\mu_{i}+\varepsilon_{it}$, with the number of uploads of
user $i$ per month $t$ as the dependent variable. The dots reflect month-
specific point estimates comparing treated users to the control group.
Standard errors are clustered at the user level, and bars indicate 90%
confidence bands. In both panels, the vertical line indicates the creation of
LITE 1.0.0.

In the following specifications, we focus on the number of new uploads as our
dependent variable. We compare users that had at least one image in the LITE
dataset to our control group. For this analysis, we can draw upon high-
frequency information, which allows us to construct a panel dataset at the
user-month level. In Panel A of Figure 1, we plot the average number of
uploads per month in the treatment group and the control group. In the
observed 18 months before the creation of the LITE dataset (indicated by a 0
on the horizontal axis), upload rates do not fluctuate much for both. Treated
users upload on average around 3 images per month and control users upload
around 1 image per month. Looking at differences between treated and control
in Panel B of Figure 1 confirms that there are no statistically significant
differences in the before period. However, we see a substantial and
statistically significant decrease in the after period. The effect is
immediate and remains relatively stable over time. After 24 months, there
seems to be a further decrease, which we investigate further in section 5.4.

Table 3: Results: Changes in uploads

| (1) | (2) | (3)
---|---|---|---
| Uploads | I(Uploads$>$0) | Log(1+Uploads)
Post $\times$ Treated | -1.1099∗∗∗ | -0.0691∗∗∗ | -0.1630∗∗∗
| (0.09693) | (0.00352) | (0.00728)
Mean Treated Before | 2.9038 | 0.2635 | 0.4892
Month FE | Yes | Yes | Yes
User FE | Yes | Yes | Yes
Observations | 612,662 | 612,662 | 612,662

Note: The dependent variable in column (1) is the total number of uploads per
month, in column (2) it is an indicator of whether a user account has uploaded
at least one image in a given month, and it is the log number of uploads in
column (3). Treated indicates whether a user was part of the LITE dataset.
Fixed effects for upload month of the image and user account. Standard errors
clustered at the user level in parentheses. ∗ $p<0.10$, ∗∗ $p<0.05$ ∗∗∗
$p<0.01$

We can quantify the average treatment effect for the entire observed post-
period in an OLS model specified as described in equation (1). The results are
reported in Table 3. In column (1), we look at the number of uploaded photos
per month and quantify the average treatment effect as 1 less upload per
month, which is a reduction of about 38%. We get similar results when we apply
a log transformation in column (3). Further, in column (2), we document a
decrease in the extensive margin of about 30%, i.e., when we use a dependent
variable that indicates whether a user has uploaded at least one image in a
given month.

###  5.2 Changes by different types of users

####  5.2.1 Professional versus amateur photographers

Beyond the average treatment effect on the treated, investigating
heterogeneity in user characteristics can provide further nuance. Hence, we
consider whether behavior differs between professional and amateur users.
Thereafter, we turn to differences between more or less prolific users.

Table 4: Results: Changes in uploads, professionals versus amateurs

| (1) | (2)
---|---|---
Post $\times$ Treated | -0.6740∗∗∗ | -0.7206∗∗∗
| (0.13433) | (0.09401)
Post $\times$ Pro-Gear | -0.0686 |
| (0.10904) |
Post $\times$ Treated $\times$ Pro-Gear | -0.5038∗∗∗ |
| (0.17407) |
Post $\times$ ForHire |  | -0.2362
|  | (0.16669)
Post $\times$ Treated $\times$ ForHire |  | -0.8917∗∗∗
|  | (0.25200)
Mean DV | 1.4595 | 1.4474
Month FE | Yes | Yes
User FE | Yes | Yes
Observations | 611,891 | 577,739

Note: The dependent variable is the number of uploads in a given month.
Treated indicates whether a user was part of the LITE dataset. Pro-Gear
indicates whether the user has uploaded at least one photo taken with
professional gear (see section 4 for a detailed definition). ForHire indicates
whether a user has chosen to show “Available for hire” on their Unsplash
profile page. The number of observations differs from those in Table 3 because
we do not observe Pro-Gear for all images and ForHire for all users. Fixed
effects for upload month of the image and user account. Standard errors
clustered at the user level in parentheses. ∗ $p<0.10$, ∗∗ $p<0.05$ ∗∗∗
$p<0.01$ ∗ $p<0.10$, ∗∗ $p<0.05$ ∗∗∗ $p<0.01$

The results in column (1) of Table 4 show that treated users who upload
photographs created with professional gear react even more negatively than
others. Similarly, we find in column (2) that treated users with a badge on
their profile page that says “Available for hire” reduced their uploads
disproportionally compared to users without a badge. It is possible that users
with professional gear and those who signal that they are available for hire
make a living as photographers and are, therefore, perhaps more directly
affected by the potential displacement effects of AI. Hence, monetary motives
may be one of the drivers for users to contribute less to the platform after
their works have been made available for AI research.

####  5.2.2 Heterogeneity in contribution intensity

The distinction between professional and amateur photographers is helpful to
understand the societal impact of AI. Another interesting dimension of user
heterogeneity concerns upload activity, which is an important metric to draw
insights for platform governance. In Table 5, we show that the main effect
reported in column (1) of Table 3 varies by percentile of a user’s total
upload activity. The point estimates in columns (1)–(4) of Table 5 are smaller
and not always significantly different from the estimate in column (1) of
Table 3 in the sense that 90% confidence bands overlap. However, in absolute
terms, the implied effect sizes are larger (column 2: -45%, column 3: -44% and
column 4: -40%) compared to the -30% of the baseline. Given that we observe
the universe of activity on Unsplash, we prefer not to remove particularly
prolific users and report estimates of average treatment effects based on all
users as our more conservative baseline results.

Table 5: Results: Changes in uploads, by user activity

| (1) | (2) | (3) | (4)
---|---|---|---|---
| All | w/o 99th | w/o 95th | w/o 90th
Post $\times$ Treated | -0.4529∗∗∗ | -0.9872∗∗∗ | -0.6847∗∗∗ | -0.4571∗∗∗
| (0.02672) | (0.05445) | (0.03513) | (0.02699)
Post $\times$ Treated $\times$ Above99thPct | -17.4338 |  |  |
| (11.68857) |  |  |
Post $\times$ Treated $\times$ Above95thPct | -0.9941 |  |  |
| (2.04651) |  |  |
Post $\times$ Treated $\times$ Above90thPct | -1.3862∗ |  |  |
| (0.74521) |  |  |
Mean Treated Before | 2.9038 | 2.2160 | 1.5488 | 1.1435
Month FE | Yes | Yes | Yes | Yes
User FE | Yes | Yes | Yes | Yes
Observations | 612,662 | 606,490 | 581,988 | 551,076

Note: The dependent variable is the number of uploads in a given month.
Treated indicates whether a user was part of the LITE dataset. Above99thPct,
Above95thPct and Above90thPct indicate whether the users’ uploads were above
the 99/95/90th percentile. The samples in columns (2) to (4) do not include
users above the 99/95/90th percentile of uploads. ∗ $p<0.10$, ∗∗ $p<0.05$ ∗∗∗
$p<0.01$

###  5.3 Changes in the types of images

####  5.3.1 Nature-themed and curated images

Table 6: Results: Changes in uploads, by types of images

| (1) | (2) | (3)
---|---|---|---
| Nature | Curated | Nature & Curated
Post $\times$ Treated | -0.2265∗∗∗ | -0.3472∗∗∗ | -0.1392∗∗∗
| (0.02956) | (0.01642) | (0.00614)
Mean Treated Before | 0.7218 | 0.5739 | 0.2022
Month FE | Yes | Yes | Yes
User FE | Yes | Yes | Yes
Observations | 612,662 | 612,662 | 612,662

Note: The dependent variable is an indicator of whether a user account has
uploaded at least one image in a given month. Treated indicates whether a user
was part of the LITE dataset. Fixed effects for upload month of the image and
user account. Standard errors clustered at the user level in parentheses. ∗
$p<0.10$, ∗∗ $p<0.05$ ∗∗∗ $p<0.01$

Next, we study whether the types of images uploaded to Unsplash have changed.
In column (1) of Table 6, we show that the likelihood of uploading at least
one nature-themed image decreases by 23 percentage points or 30% relative to
the average in the before period. Note that this is about the same effect size
we get when looking at all types of images in column (2) of Table 3. However,
columns (2) and (3) of Table 6, suggest that the extensive margin decreases at
60% for curated images and at 70% for nature-themed and curated images. If we
believe that Unsplash’s curation team picks high-quality images, then this
suggests a reduction in the artistic quality of uploads.

####  5.3.2 Analysis of Variety and Novelty measures

Figure 2: Changes in uploads, similarity of images

Panel A: Levels of average similarity

Panel B: Differences in average similarity

Panel C: Levels of very similar count

Panel D: Differences in very similar count

Note: In Panel A, we depict the average similarity of uploaded images by users
in the treatment group (solid) and by users in the control group (dashed). In
Panel C, we plot the number of images uploaded by treated users (solid) and
control users (dashed) that are very similar to the existing stock (threshold
0.8). In Panels B and D, we plot OLS estimates of the $\delta_{\tau}$
coefficients are obtained from $Y_{it}=\sum_{\tau\in
T}\nu_{\tau}+\sum_{\tau\in T}\delta_{\tau}\left(\gamma_{\tau}\times
Treated_{i}\right)+\varepsilon_{it}$, with the number of uploads of user $i$
per month $t$ as the dependent variable. The dots reflect month-specific point
estimates comparing treatment users to control users. Standard errors are
clustered at the user level, and bars indicate 90% confidence bands. In all
panels, the vertical line indicates the creation of LITE 1.0.0, and numbers on
the horizontal axis indicate the number of months before and after that.

Table 7: Results: Changes in uploads, similarity of images

| Avg. Similarity | LogVerySimilar (0.8) | LogVerySimilar (0.9)
---|---|---|---
| (1) | (2) | (3) | (4) | (5) | (6)
Post $\times$ Treated | 0.0189∗∗∗ | 0.0070∗∗ | 0.2520∗∗∗ | 0.0749 | 0.1336∗∗∗ | 0.0380
| (0.00097) | (0.00345) | (0.01817) | (0.05707) | (0.02300) | (0.06871)
Mean Treated Before | 0.3910 | 0.3910 | 8.1285 | 8.1285 | 4.5527 | 4.5527
Image-Age FE | Yes | Yes | Yes | Yes | Yes | Yes
User FE | No | Yes | No | Yes | No | Yes
Observations | 340,991 | 340,346 | 340,991 | 340,346 | 340,991 | 340,346

Note: The dependent variable in columns (1) and (2) measures how similar an
uploaded image is relative to the average image in the stock of images
uploaded by treated vs. control users up until one year before the creation of
the LITE dataset. In columns (3)–(6), the dependent variable is the log+1
count of images from the stock of images uploaded by treated/control users up
until one year before the creation of the LITE dataset that have a cosine
similarity score of larger than 0.8 or 0.9 relative an uploaded image. Treated
indicates whether a user was part of the LITE dataset. Fixed effects for
upload month of the image and user account in columns (2), (4) and (6).
Standard errors in parentheses, White-robust in columns (1), (3) and (5), and
clustered at the user level in columns (2), (4) and (6). ∗ $p<0.10$, ∗∗
$p<0.05$ ∗∗∗ $p<0.01$

Next, we study how strategic behavior affects the variety and novelty of
contributions more directly. In Table 7, we report results from our analysis
of similarity using natural language processing of user-specified keywords of
uploaded images. We use the same difference-in-differences identification
strategy as above. Panel A of Figure 2 shows that the unconditional mean of
the average similarity to the pre-existing stock of photos decreases less for
images uploaded by treated users than for images uploaded by control users. In
other words, treated users upload images that are less novel and more similar
to the existing stock of images. This is confirmed more formally in Panel B,
where we plot estimates of the difference between the treatment and control
group in each month, before and after the release of the LITE dataset, from a
model without user-fixed effects. In Panels C and D, we turn to a count of the
number of very similar images. Again, we see that the decrease in very similar
images uploaded by users in the treatment group is less steep than in the
control group.

We quantify these differences in Table 7. We find that it is not users who
change the type of images they upload, but a difference in the type of
uploaded image is coming from a change in the composition of users. In the
specifications with user-fixed effects in columns (2), (4) and (6), we do not
find much evidence that the release of the LITE dataset caused new uploads to
differ from existing uploads. This is especially the case concerning the
number of highly similar images in the existing stock. Hence, users do not
change the types of images they upload. However, looking at the results from
specifications without user-fixed effects in columns (1), (3) and (5), we do
see that uploaded images become more similar to the existing stock. Column (1)
suggests an increase of about 5% in average similarity for uploads by treated
users, whereas columns (3) and (5) suggest an increase of about 28% and 13%,
respectively, in the number of highly similar images. Overall, the results
imply that the composition of the training dataset will change because of
changes in the user composition and the types of images they upload, not
because individual users change the types of images they upload. This is in
line with the results we describe above. In what follows, we explore potential
mechanisms that may explain the changes in user behavior.

###  5.4 Mechanisms: Awareness of AI potential and treatment intensity

Table 8: Results: Changes in uploads, mechanisms

| Uploads
---|---
| (1) | (2)
Post $\times$ Treated |  -1.0056∗∗∗ |
| (0.10508) |
PostAug22 $\times$ Treated |  -0.4054∗∗∗ |
| (0.10056) |
Post $\times$ TreatedSingle |  |  -0.3059∗∗∗
|  | (0.09750)
Post $\times$ TreatedMultiple |  |  -2.2830∗∗∗
|  | (0.17248)
Mean Treated Before | 2.9038 |
Mean TreatedSingle Before |  | 1.4011
Mean TreatedMultiple Before |  | 5.0982
Month FE | Yes | Yes
User FE | Yes | Yes
Observations | 612,662 | 612,662

Note: The dependent variable is the total number of uploads per month. Post
indicates the entire period after the creation of LITE and PostAug22 indicates
the period after August 2022, when groundbreaking genAI for images was
released. Treated indicates whether a user was part of the LITE dataset.
TreatedSingle indicates whether a user had exactly one image in the LITE
dataset. TreatedMultiple indicates whether a user had more than one image in
the LITE dataset. Fixed effects for upload month of the image and user
account. Standard errors clustered at the user level in parentheses. ∗
$p<0.10$, ∗∗ $p<0.05$ ∗∗∗ $p<0.01$

The results in Table 8 serve to highlight potential mechanisms through which
the inclusion in the LITE dataset induces users to reduce their upload
activity. In column (1), informed by Figure 1, we split the post-period in two
parts. The first 24 months correspond to the period between June 2020 and
August 2022, a period with modest public interest in AI and AI art in
particular. After August 2022, however, there was a substantial increase in
media coverage on the topic, e.g., a New York Times article titled “We Need to
Talk About How Good A.I. Is Getting”, which may have further raised awareness
about genAI in the Unsplash community.202020see
https://www.nytimes.com/2022/08/24/technology/ai-technology-progress.html The
timing also coincides with the public release of Stable Diffusion, the first
text-to-image generator that could be run on consumer-grade machines,
democratizing access to genAI technology after similar technology released a
few months earlier had only restricted public access (e.g. DALL-E and
Midjourney).212121See https://stability.ai/news/stable-diffusion-announcement.
We find a treatment effect of 34% before August 2022 and 49% thereafter, which
corresponds to a significant 40% increase in the treatment effect. This
suggests that, as awareness of the commercial potential of image generation AI
raises, photographers reduce their contributions to Unsplash and, therefore,
to the flow of AI training data.

In column (2), we distinguish between users that were affected more or less
intensively by the release of the LITE dataset. In particular, we distinguish
between users who had one image included (about 60%) and users who had
multiple images included (about 40%). Conditional on more than one image in
the LITE dataset, the average number of images per user is 5.96 (standard
deviation 14.18 and maximum 419). The estimated effect size is 22% for single-
treated users and 45% for multiple-treated users. This difference is
statistically significant. These results provide further evidence that users
must have been aware of their images being included in the LITE dataset
because otherwise, we would not expect a stronger response from more heavily
treated users. This result has interesting implications for policy, suggesting
that a one-size-fits-all approach to licensing of human-generated work for AI
training datasets can lead to unintended heterogeneous effects on the flow of
training data. As a consequence, our results suggest that a licensing scheme
that ensures a continued flow of data may need to compensate more active
contributors to a larger degree (Wang et al., 2024).

Finally, it is worth noting that we observe that users in the treatment group
are three times more likely to be contributors in the Unsplash+ program
(t-statistic in a two-sided t-test is 6.13). This program enables users to
gain monetary compensation and explicitly disallows the use of their works for
AI training. Hence, we interpret the substantially higher uptake of Unsplash+
among users who had at least one image in the LITE dataset as a strategic
response to counteract economic forces and to avoid the utilization of their
works in AI applications.

###  5.5 Robustness

####  5.5.1 Alternative control group definition

Table A.1 repeats the specification of our baseline estimations in Table 3 but
with different definitions of the control group. In columns (1)–(3), we report
results using a control group composed of users that did not have nature-
themed (i.e., not with the keyword “nature”) and curated images at the time
when the LITE dataset was created. The effect size for the number of uploads
is 47% in column (1), 33% in terms of the extensive margin in column (2), and
19% when using a specification with the log-transformed number of uploads in
column (3). In columns (4)–(6), we use a control group comprised of all users
whose images were not included in the LITE dataset. The effect sizes are 50%,
40% and 21%, respectively. Note that the estimates reported in Table A.1 are
not significantly different from those reported in Table 3 in the sense that
90% confidence bands overlap.

####  5.5.2 OLS versus non-linear models

Given that our outcome variable is both a count, i.e. a non-negative integer,
and zero in 86.6% of the observations, one might be worried that OLS is not
the optimal choice of estimator (Wooldridge, 2023). Table A.2 shows that our
baseline results are broadly robust to using a Poisson pseudo-maximum
likelihood regression (PPML) approach.222222We do not report results from a
negative binomial model because PPML is preferred for its robustness
(Blackburn, 2015). However, the results indicate that PPML is sensitive to
large values of the DV, which is of economic interest in our setting (i.e.
heavy platform users). In addition, the interaction effect in non-linear
models cannot be directly interpreted as the average treatment effect on the
treated (Ai and Norton, 2003; Puhani, 2012) while simultaneously imposing
additional restrictions for parallel trends to hold (Roth and Sant’Anna,
2023). For these reasons, and for simplicity in the interpretation of effect
sizes, we report estimates from OLS regressions throughout.

##  6 Discussion

###  6.1 Back-of-the-envelope estimation of aggregate effects

Having estimated the average treatment effects following the release of the
LITE dataset on several key outcomes, we can now use these estimations to
simulate several counterfactuals in which we vary the proportion of users
treated.

To proceed, we first need to define a few key variables. The share of users
included in the LITE dataset is $\lambda$, the effect of being in the LITE
dataset is $\hat{\delta}$, and the factual growth rate of the number of images
per user in the post-period is $g$. With this, we can calculate a
counterfactual growth rate $\bar{g}=g/(1-\lambda+\lambda\hat{\delta})$, which
is the growth rate that would have materialized had the LITE dataset not been
released. Then, we can use a few descriptive statistics as well as our causal
estimates to attempt a back-of-the-envelope calculation of counterfactuals of
key outcomes such as the total number of uploads. Finally, once we have
calculated $\bar{g}$, we can simulate any growth rate
$\tilde{g}(\lambda)=(1-\lambda)(1+\bar{g})+\lambda(1+\hat{\delta}\bar{g})-1$.
This is useful to calculate counterfactuals where we can vary the size of the
LITE training dataset and approximate how the size of the LITE training
dataset affects the flow of data in terms of the aggregate number of data
points added to Unsplash.

To simulate the impact of the share of treated users on the number of uploads,
we use the estimated average treatment effect on the treated and the mean
uploads of treated users from Table 3. This leads to
$\hat{\delta}=1-(1.1099/2.9038)=0.6$. When the LITE dataset was created in
June 2020, Unsplash had a stock of $S_{0}=1,931,324$ images uploaded by
$U_{0}=198,792$ users. The LITE dataset encompasses $U_{L}=8,298$ users, such
that the share of users in the LITE dataset was $\lambda=U_{L}/U_{0}=0.042$.
About three years later, in May 2023, Unsplash has $S_{t}=4,960,120$ images
and $U_{t}=338,635$ users. Hence, the factual growth rate
$g=\frac{S_{t}}{U_{t}}/\frac{S_{0}}{U_{0}}-1=14.65/9.72-1=0.51$ and therefore
the counterfactual growth rate $\bar{g}=(1.51)/(1-0.042+0.042\times
0.6)=0.52$. In absolute terms, without the release of the LITE dataset, users
would have uploaded 19,313 more images to Unsplash ($=S_{0}(0.52-0.51)$).

We can now extrapolate that a twice-as-large LITE dataset would have reduced
the flow of data by 2% (=1-0.50/0.51) compared to the factual and making the
entire catalog available for commercial AI research would have reduced the
flow of data by 39% (=1-0.31/0.51), corresponding to 386,264 images uploaded
less over the period.

###  6.2 How different is Unsplash from other platforms?

To provide some insight into this question, it is useful to compare Unsplash
to Instagram, the by far largest image-sharing platform. According to
Instagram’s terms of service, users retain ownership of any intellectual
property rights that they hold in that content. However, by posting content to
Instagram, a user grants the platform a non-exclusive, royalty-free,
transferable, sublicensable, worldwide license to use the content they
post.232323See https://help.instagram.com/478745558852511/. This means
Instagram can use, reproduce, modify, display, and distribute the content, and
they can also allow others to do so within their platform or in association
with other media. This license ends when a user deletes the content or the
account unless the content has been shared with others and they have not
deleted it. Unsplash operates in a similar fashion yet in an even more
permissive fashion. When a user uploads an image to Unsplash, they grant
anyone a license to use their image for free, including for commercial
purposes, without requiring permission from or providing attribution to the
user.242424See https://unsplash.com/license. By uploading photos to Unsplash,
users agree to this license, which effectively allows anyone worldwide to use
the images with very few restrictions. It is important to note that while a
user still retains the copyright to their image, the rights they grant through
the Unsplash license are much broader in scope and do not end even if they
remove the image from the platform. A key difference there is the control over
use. On Instagram, the use of an uploaded image is mostly within the context
of the platform, though the license a user grants Instagram is quite broad.
Unsplash, in contrast, allows anyone to use the images for nearly any purpose,
including commercially and outside the platform, without any compensation or
credit to the uploader. Instagram’s terms are not explicitly clear about
commercial use by third parties unless it is sublicensed, while Unsplash
explicitly allows commercial use by anyone.

Table 9: Results: Changes in uploads, Unsplash vs. Instagram

| Unsplash | Instagram | Both
---|---|---|---
| (1) | (2) | (3) | (4)
Treated $\times$ Post | -0.5118∗∗∗ | -0.0013 | -0.4344∗∗∗ | -0.5111∗∗∗
| (0.08951) | (0.02326) | (0.06900) | (0.06537)
Instagram |  |  | 0.0704 |
|  |  | (0.06441) |
Treated $\times$ Instagram |  |  | -0.7933∗∗∗ |
|  |  | (0.07820) |
Post $\times$ Instagram |  |  | 0.0003 | -0.0093
|  |  | (0.07802) | (0.07604)
Treated $\times$ Post $\times$ Instagram |  |  | 0.3556∗∗∗ | 0.5090∗∗∗
|  |  | (0.09457) | (0.09240)
Constant | 0.9318∗∗∗ | 0.3874∗∗∗ | 0.8952∗∗∗ | 0.6627∗∗∗
| (0.04588) | (0.01192) | (0.03553) | (0.03525)
Total effect Instagram |  |  |  |
lincom |  |  | -0.0787 | -0.0020
|  |  | (0.06900) | (0.06537)
Mean DV | 1.4300 | 0.7071 | 1.0686 | 1.0686
Month FE | Yes | Yes | Yes | Yes
User FE | Yes | Yes | Yes | No
User-Platform FE | No | No | No | Yes
Observations | 56,855 | 56,855 | 113,728 | 113,728

Note: The dependent variable is the number of monthly uploads to either
Unsplash or Instagram, or both. Treated indicates whether a user was part of
the LITE dataset. Standard errors clustered at the user level in parentheses.
∗ $p<0.10$, ∗∗ $p<0.05$ ∗∗∗ $p<0.01$ ∗

In Table 9, we study whether users’ reaction to having their images included
in an AI training dataset affects only their uploads to Unsplash, or whether
they also change their behavior on Instagram. To do so, we compare the number
of uploads of treated versus control users on Unsplash to their uploads on
Instagram. In column (1), we see that the average treatment effect with
respect to uploads on Unsplash is about half the size of our baseline (column
1 in Table 3) in this restricted sample. In column (2), we do not find a
significant effect on Instagram uploads, as the point estimate is very close
to zero. In columns (3) and (4), we combine information from both platforms to
compare effect sizes directly. The linear combination in both sets of results,
with user fixed effects and with user-platform fixed effects, again indicates
that the effect on Instagram is not significant. This implies that users did
not switch to Instagram as a new outlet for their photography work, but it
also does not imply that users stopped to publish their work anywhere online.
Overall, our results suggest that adding Unsplash images to an Unsplash-
curated AI training dataset affects user behavior on Unsplash, but not
elsewhere.

##  7 Conclusion

GenAI models benefit from the abundance of available training data on the
internet. However, the economics of the supply of data have not been discussed
much. In this research, we focus on how strategic behavior of human creators
affects the flow and, therefore, the future stock of AI training data.

We study the empirical setting of creators who openly contribute their work to
Unsplash, a stock photography platform that provides crowd-sourced images
under a permissive license. In particular, we evaluate the effects of the
release of a training dataset for commercial AI applications. We find that
affected users are more likely to leave the platform. Those that stay, reduce
their uploads substantially. As a result, the size of the flow of data
changes, as does the content. Our results imply that the release of a free
training dataset for commercial AI applications, covering roughly 5% of the
platform’s contributors, overall reduced the flow of data by 2%, decreased the
variety (deviation from the average concept in the stock) in the dataset by 4%
and increased novelty (repetition of very similar concepts in the stock) by
2.5% over the course of three years.

Our results have important implications for the debate on copyright and AI.
Even in a setting where copyright does not directly factor in – Unsplash
caters to a special set of creators that choose an open license –, we see a
sizable change in individuals’ behavior when their work is made available as
an AI training dataset. Our results suggest that a change in the flow of data
can also translate into changes in aspects of dataset quality, such as variety
and novelty, because of changes in the user composition. Consequently, this
has implications for the quality of AI training datasets and possibly AI
output (Hadsell et al., 2020; He et al., 2011; Quinn and Gutt, 2023; Whang et
al., 2023).

In conclusion, our results raise the question of whether exemptions in
copyright law for AI (e.g. through fair use provisions that do not compensate
rightsholders) could lead society to a situation of lower quality AI (smaller
and less varied datasets) and less interesting (because less varied and more
repetitive) human creations. Hence, while AI developers may initially prefer
laissez-faire copyright (e.g., fair use or text-and-data-mining exceptions),
the upstream consequences on the flow of data, both in terms of quantity and
quality, may have negative consequences for the continued quality of existing
AI applications and raise difficulties in the development of new technologies.

More research is needed to guide policymakers to carefully weigh the trade-
offs between regulation of the supply of data, e.g. through privacy policy,
competition policy, or copyright law, and AI innovation.

## References

  * Acemoglu et al. (2022) Acemoglu, D., Autor, D., Hazell, J., and Restrepo, P. (2022). “Artificial intelligence and jobs: Evidence from online vacancies.” Journal of Labor Economics, 40, S293–S340.
  * Ai and Norton (2003) Ai, C., and Norton, E. C. (2003). “Interaction terms in logit and probit models.” Economics Letters, 80(1), 123–129.
  * Bauer et al. (2016) Bauer, J., Franke, N., and Tuertscher, P. (2016). “Intellectual Property Norms in Online Communities: How User-Organized Intellectual Property Regulation Supports Innovation.” Information Systems Research, 27(4), 724–750.
  * Beraja et al. (2023) Beraja, M., Yang, D. Y., and Yuchtman, N. (2023). “Data-intensive innovation and the state: Evidence from ai firms in china.” The Review of Economic Studies, 90(4), 1701–1723.
  * Birhane and Prabhu (2021) Birhane, A., and Prabhu, V. U. (2021). “Large image datasets: A pyrrhic win for computer vision?” In 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 1536–1546, IEEE.
  * Blackburn (2015) Blackburn, M. L. (2015). “The relative performance of poisson and negative binomial regression estimators.” Oxford Bulletin of Economics and Statistics, 77(4), 605–616.
  * Brynjolfsson et al. (2023) Brynjolfsson, E., Li, D., and Raymond, L. R. (2023). “Generative AI at Work.” NBER Working Paper, (31161).
  * Burtch et al. (2024) Burtch, G., Lee, D., and Chen, Z. (2024). “Generative ai degrades online communities.” Communications of the ACM, 67(3), 40–42.
  * del Rio-Chanona et al. (2023) del Rio-Chanona, M., Laurentsyeva, N., and Wachs, J. (2023). “Are large language models a threat to digital public goods? evidence from activity on stack overflow.” Working Paper.
  * Doshi and Hauser (2023) Doshi, A. R., and Hauser, O. (2023). “Generative ai enhances individual creativity but reduces the collective diversity of novel content.” Working Paper.
  * Eloundou et al. (2023) Eloundou, T., Manning, S., Mishkin, P., and Rock, D. (2023). “Gpts are gpts: An early look at the labor market impact potential of large language models.” arXiv preprint arXiv:2303.10130.
  * Eshraghian (2020) Eshraghian, J. K. (2020). “Human ownership of artificial creativity.” Nature Machine Intelligence, 2(3), 157–160.
  * Farboodi et al. (2019) Farboodi, M., Mihet, R., Philippon, T., and Veldkamp, L. (2019). “Big data and firm dynamics.” In AEA Papers and Proceedings, vol. 109, 38–42, JSTOR.
  * Felten et al. (2023) Felten, E. W., Raj, M., and Seamans, R. (2023). “How will language modelers like chatgpt affect occupations and industries?” SSRN Working Paper.
  * Fiil-Flynn et al. (2022) Fiil-Flynn, S. M., Butler, B., Carroll, M., Cohen-Sasson, O., Craig, C., Guibault, L., Jaszi, P., Jütte, B. J., Katz, A., Quintais, J. P., Margoni, T., de Souza, A. R., Sag, M., Samberg, R., Schirru, L., Senftleben, M., Tur-Sinai, O., and Contreras, J. L. (2022). “Legal reform to enhance global text and data mining research.” Science, 378(6623), 951–953.
  * Gallea (2023) Gallea, Q. (2023). “From mundane to meaningful: Ai’s influence on work dynamics–evidence from chatgpt and stack overflow.” arXiv preprint arXiv:2308.11302.
  * Gans (2024) Gans, J. (2024). “Copyright policy options for generative artificial intelligence.” NBER Working paper, (32106).
  * Godinho de Matos and Adjerid (2022) Godinho de Matos, M., and Adjerid, I. (2022). “Consumer Consent and Firm Targeting After GDPR: The Case of a Large Telecom Provider.” Management Science, 68(5), 3330–3378.
  * Guilbeault et al. (2024) Guilbeault, D., Delecourt, S., Hull, T., Desikan, B. S., Chu, M., and Nadler, E. (2024). “Online images amplify gender bias.” Nature, 1–7.
  * Hadsell et al. (2020) Hadsell, R., Rao, D., Rusu, A. A., and Pascanu, R. (2020). “Embracing change: Continual learning in deep neural networks.” Trends in cognitive sciences, 24(12), 1028–1040.
  * He et al. (2011) He, H., Chen, S., Li, K., and Xu, X. (2011). “Incremental learning from stream data.” IEEE Transactions on Neural Networks, 22(12), 1901–1914.
  * Henderson et al. (2023) Henderson, P., Li, X., Jurafsky, D., Hashimoto, T., Lemley, M. A., and Liang, P. (2023). “Foundation models and fair use.” arXiv preprint arXiv:2303.15715.
  * Ho et al. (2024) Ho, A., Besiroglu, T., Erdil, E., Owen, D., Rahman, R., Guo, Z. C., Atkinson, D., Thompson, N., and Sevilla, J. (2024). “Algorithmic progress in language models.” (2403.05812).
  * Huang et al. (2023) Huang, H., Fu, R., and Ghose, A. (2023). “Generative AI and Content-Creator Economy: Evidence from Online Content Creation Platforms.” SSRN Working Paper.
  * Johnson (2022) Johnson, G. (2022). “Economic research on privacy regulation: Lessons from the gdpr and beyond.”
  * Jones and Tonetti (2020) Jones, C. I., and Tonetti, C. (2020). “Nonrivalry and the economics of data.” American Economic Review, 110, 2819–58.
  * Lei et al. (2023) Lei, X., Chen, Y., and Sen, A. (2023). “The value of external data for digital platforms: Evidence from a field experiment on search suggestions.” Available at SSRN.
  * Levendowski (2018) Levendowski, A. (2018). “How copyright law can fix artificial intelligence’s implicit bias problem.” Wash. L. Rev., 93, 579.
  * Lin (2024) Lin, S. (2024). “From creation to caution: The effect of ai on online art market.” Working Paper.
  * Martens et al. (2024) Martens, B., Parker, G., Petropoulos, G., and Van Alstyne, M. W. (2024). “Towards efficient information sharing in network markets.” In Proceedings of the 57th Hawaii International Conference on System Sciences.
  * McElheran et al. (2024) McElheran, K., Li, J. F., Brynjolfsson, E., Kroff, Z., Dinlersoz, E., Foster, L., and Zolas, N. (2024). “AI adoption in America: Who, what, and where.” Journal of Economics & Management Strategy, 108(11), 3451–3491.
  * Metz et al. (2024) Metz, C., Kang, C., Frenkel, S., Thompson, S. A., and Grant, N. (2024). “How tech giants cut corners to harvest data for a.i.” The New York Times, updated April 8, 2024. Reporting from San Francisco, Washington, and New York.
  * Neumann et al. (2019) Neumann, N., Tucker, C. E., and Whitfield, T. (2019). “How effective is third-party consumer profiling? evidence from field studies.” Marketing Science, 38(6), 918–926.
  * Peukert et al. (2023) Peukert, C., Sen, A., and Claussen, J. (2023). “The editor and the algorithm: Recommendation technology in online news.” Management Science, forthcoming.
  * Peukert and Windisch (2024) Peukert, C., and Windisch, M. (2024). “The economics of copyright in the digital age.” Journal of Economic Surveys, forthcoming.
  * Prüfer and Schottmüller (2021) Prüfer, J., and Schottmüller, C. (2021). “Competing with big data.” The Journal of Industrial Economics, 69(4), 967–1008.
  * Puhani (2012) Puhani, P. A. (2012). “The treatment effect, the cross difference, and the interaction term in nonlinear “difference-in-differences” models.” Economics Letters, 115(1), 85–87.
  * Quinn and Gutt (2023) Quinn, M., and Gutt, D. (2023). “Does generative ai erode its own training data? empirical evidence of the effects on data quantity and characteristics from a q&a platform.” SSRN Working Paper.
  * Roth and Sant’Anna (2023) Roth, J., and Sant’Anna, P. H. C. (2023). “When is parallel trends sensitive to functional form?” Econometrica, 91(2), 737–747.
  * Samuelson (2023) Samuelson, P. (2023). “Generative ai meets copyright.” Science, 381(6654), 158–161.
  * Sokol and Van Alstyne (2021) Sokol, D. D., and Van Alstyne, M. (2021). “The rising risk of platform regulation.” MIT Sloan Management Review, 62(2), 6A–10A.
  * Sun et al. (2023) Sun, T., Yuan, Z., Li, C., Zhang, K., and Xu, J. (2023). “The value of personal data in internet commerce: A high-stake field experiment on data regulation policy.” Management Science, forthcoming.
  * Valavi et al. (2022) Valavi, E., Hestness, J., Ardalani, N., and Iansiti, M. (2022). “Time and the value of data.” arXiv preprint arXiv:2203.09118.
  * Wang et al. (2024) Wang, J. T., Deng, Z., Chiba-Okabe, H., Barak, B., and Su, W. J. (2024). “An economic solution to copyright challenges of generative ai.” arXiv:2404.13964 [cs.LG].
  * Whang et al. (2023) Whang, S. E., Roh, Y., Song, H., and Lee, J.-G. (2023). “Data collection and quality challenges in deep learning: A data-centric ai perspective.” The VLDB Journal, 32(4), 791–813.
  * Wooldridge (2023) Wooldridge, J. M. (2023). “Simple approaches to nonlinear difference-in-differences with panel data.” The Econometrics Journal, 26(3), C31–C66.
  * Wu et al. (2020) Wu, L., Hitt, L., and Lou, B. (2020). “Data analytics, innovation, and firm productivity.” Management Science, 66(5), 2017–2039.
  * Yang and Zhang (2024) Yang, S. A., and Zhang, A. H. (2024). “Generative ai and copyright: A dynamic perspective.” arXiv preprint arXiv:2402.17801.

## Appendix

Table A.1: Results: Changes in uploads, alternative control group definitions

| Not Nature & Curated | Not in LITE
---|---|---
| (1) | (2) | (3) | (4) | (5) | (6)
| Uploads |  I(Upl.$>$0) | Log(1+Upl.) | Uploads |  I(Upl.$>$0) | Log(1+Upl.)
Post $\times$ Treated |  -1.3737∗∗∗ |  -0.0857∗∗∗ |  -0.2072∗∗∗ |  -1.4495∗∗∗ |  -0.1041∗∗∗ |  -0.2323∗∗∗
| (0.08534) | (0.00238) | (0.00539) | (0.08527) | (0.00236) | (0.00537)
Mean Treated Before | 2.9038 | 0.2635 | 0.4892 | 2.9038 | 0.2635 | 0.4892
Month FE | Yes | Yes | Yes | Yes | Yes | Yes
User FE | Yes | Yes | Yes | Yes | Yes | Yes
Observations | 7,666,123 | 7,666,123 | 7,666,123 | 9,704,756 | 9,704,756 | 9,704,756

Note: The dependent variable in columns (1) and (4) is the total number of
uploads per month, in columns (2) and (5) it is an indicator of whether a user
account has uploaded at least one image in a given month, and it is the log
number of uploads in columns (3) and (6). The control group in columns (1)–(3)
includes users that have uploaded at least one non-curated and non-nature
themed image. The control group in columns (4)–(6) includes all users that do
not appear in the LITE data. Treated indicates whether a user was part of the
LITE dataset. Fixed effects for upload month of the image and user account.
Standard errors clustered at the user level in parentheses. ∗ $p<0.10$, ∗∗
$p<0.05$ ∗∗∗ $p<0.01$

Table A.2: Results: Changes in uploads, Poisson models

| All | w/o 99th | w/o 95th | w/o 90th
---|---|---|---|---
| (1) | (2) | (3) | (4) | (5)
Post $\times$ Treated | 0.0607 | -0.1996∗∗∗ | -0.0541 | -0.1703∗∗∗ | -0.2008∗∗∗
| (0.12357) | (0.05497) | (0.06611) | (0.05785) | (0.05866)
Post $\times$ Treated $\times$ Above99thPct |  | -0.3154 |  |  |
|  | (0.38920) |  |  |
Post $\times$ Treated $\times$ Above95thPct |  | 0.1842 |  |  |
|  | (0.24548) |  |  |
Post $\times$ Treated $\times$ Above90thPct |  | -0.2198 |  |  |
|  | (0.16234) |  |  |
Mean Treated Before | 2.9038 | 2.9038 | 2.2160 | 1.5488 | 1.1435
Month FE | Yes | Yes | Yes | Yes | Yes
User FE | Yes | Yes | Yes | Yes | Yes
Observations | 468,555 | 468,555 | 462,383 | 437,881 | 406,969

Note: We estimate Poisson pseudo-maximum likelihood regressions models. The
dependent variable is the number of uploads in a given month. Treated
indicates whether a user was part of the LITE dataset. Above99thPct,
Above95thPct, and Above90thPct indicate whether a user’s uploads were above
the 99/95/90th percentile. The samples in columns (3) to (5) do not include
users above the 99/95/90th percentile of the pre-treatment uploads. The number
of observations is smaller than in Tables 3 and 5 because singleton
observations are dropped due to fixed effects. Standard errors clustered at
the user level in parentheses. ∗ $p<0.10$, ∗∗ $p<0.05$ ∗∗∗ $p<0.01$ ∗
$p<0.10$, ∗∗ $p<0.05$ ∗∗∗ $p<0.01$


#  _Hot-Distance_ : Combining One-Hot and Signed Boundary Distance Embeddings
for Segmentation

Marwan Zouinkhi
Janelia Research Campus
Howard Hughes Medical Institute
Ashburn, VA 20147
<EMAIL_ADDRESS>
Jeff L. Rhoades††footnotemark:
Janelia Research Campus
Howard Hughes Medical Institute
Ashburn, VA 20147
<EMAIL_ADDRESS>
Aubrey V. Weigel
Janelia Research Campus
Howard Hughes Medical Institute
Ashburn, VA 20147
<EMAIL_ADDRESS>
contributed equallycorresponding author

(June 14, 2024)

###### Abstract

Machine learning models are only as good as the data to which they are fit. As
such, it is always preferable to use as much data as possible in training
models. What data can be used for fitting a model depends a lot on the
formulation of the task. We introduce _Hot-Distance_ , a novel segmentation
target that incorporates the strength of signed boundary distance prediction
with the flexibility of one-hot encoding, to increase the amount of usable
training data for segmentation of subcellular structures in focused ion beam
scanning electron microscopy (FIB-SEM).

_Keywords_ computer vision $\cdot$ machine learning $\cdot$ image segmentation
$\cdot$ electron microscopy

##  1 Introduction

Data is the lifeblood of machine learning. It is crucial for practitioners to
maximize the accuracy and diversity of data supplied to a model during
training in order to achieve a reliable and generalizable final product. The
target a model is trained to predict determines what data can be used. For
instance, a model designed to produce segmentations may be trained to generate
one-hot encoded or signed boundary distance predictions (Heinrich et al.,
2021). A network trained to predict the presence of a given structure in terms
of a binary mask can be accurately trained on datasets indicating the objects
presence, as well as data sparsely representing its absence (see section 2.1).
However, this is not the case for signed boundary distance predictions (see
section 2.2). As a result, datasets labeling the presence of structures
mutually exclusive to the target objects, but not the targets themselves,
cannot be used to train a signed boundary distance prediction model.

We introduce _hot-distance_ to incorporate the benefits of signed boundary
distance prediction while maintaining the ability to train models on a larger
body of datasets. An empirical study of this strategy’s effects, compared to
existing methods (i.e. one-hot and signed boundary distances) is ongoing and
will be published once concluded.

##  2 Related Work

###  2.1 One-hot Encoding

One-hot encoding is named for the original representation within a simple
circuit, where a single wire in a set is activated (i.e. set to the high
voltage for the circuit), and all other wires are set low, indicating that the
input corresponds to the output indicated by the "hot" wire. This method of
encoding classes has a distinct advantage over encoding classes as an ordinal
variable (i.e. 1 for cat, 2 for dog, and 3 for bird) in that it does not imply
any relationship between classes that a network would otherwise have to learn
to decorrelate.

For instance, with one-hot encoding, instead of needing to learn to produce a
single output close to 2 for an image of a dog and near 1 for a cat, a network
can learn to set one output higher than any number of other outputs to
indicate a class. This has the additional added advantage that it allows a
network to predict class probabilities across multiple object classes
simultaneously. This also means that networks can be trained on both true
negative and true positive examples. Consider a picture of multiple animals,
which has only been labeled for pixels belonging to dogs. With a one-hot
encoding, a network can still be trained to learn what pixels do not belong to
a cat, based on the presence of the mutually exclusive dog label. Other
portions of the image, for which the true label is unknown, simply won’t
contribute to the loss calculated for the cat label. This allows partially
labeled datasets to be leveraged effectively for training.

(a)  (b)

Figure 1:  Advantages of network prediction targets. (a) One-hot embeddings
can represent true negative examples, even in the absence of known positive
examples of a class. Here, true negatives are known for the nucleus class (red
striped label) based on the true positive examples of the mutually exclusive
mitochondria class (blue label). (b) Signed tanh boundary distances present
smooth gradients for network predictions that can allow for produce instance
segmentations via watershed post-processing.

###  2.2 Signed boundary distances

In contrast to one-hot encoding, which presents a pixel-wise classification
task, signed Euclidean boundary distances constitute a pixel-wise regression
problem. Pixel-wise regression has been found empirically to outperform the
equivalent pixel-wise classification task in object segmentation (Heinrich et
al., 2018; Kainz et al., 2015). Furthermore, it allows for straightforward
post-processing into semantic segmentations, via thresholding, or even into
instance segmentations via watershed.

However, the benefit of predicting the output of a smoothly varying monotonic
function on class labels may be limited to cases in which informative
structures are within the field of view (FOV) of a network. Put another way, a
network can predict that a cat is not within a FOV, but it is likely to have a
much more difficult time predicting how far away a cat is from its FOV. A
formulation of this regression task that has seen great success thus included
a hyperbolic tangent function (tanh) as a nonlinearity to bound the distances
the network is asked to learn (Heinrich et al., 2021, 2018).

Unlike one-hot encodings, networks learning to predict signed boundary
distances cannot receive any informative signals in the absence of dense true
positive and negative labels. Without dense groundtruth labeling of a class,
no information can be provided to a network regarding the distances to
positive or negative instances of the label. This greatly reduces the pool of
usable training data and increases the barrier to entry for increasing the
amount of available data, as everything must be densely annotated.

##  3 Hot-Distance

In order to incorporate the strengths of both one-hot encodings and signed
boundary distance prediction, we introduce Hot-Distance. This involves simply
predicting both targets with the same network. Splitting of the network signal
between the class probability and signed boundary distance predictions occurs
only at the last layer of the model, meaning that almost all of the network
parameters are shared. Since one-hot predictions can be trained without dense
class annotations (for instance, from true negative instances of a class),
most network parameters can thus be accurately updated with sparse ground
truth data. By leveraging gradients from sparse data, the network backbone can
learn to properly identify object features using a much larger set of data.

Table 1: Comparison of loss functions Loss Function |  Can use
non-target crops |  Can learn gradients / do watershed
---|---|---
One-hot |  ✓ |
Signed Boundary Distance |  |  ✓
Hot-Distance |  ✓ |  ✓

##  4 Conclusion

In this paper, we introduced Hot-Distance, a novel approach that combines the
strengths of one-hot encoding and signed boundary distance embeddings for the
task of segmentation in focused ion beam scanning electron microscopy (FIB-
SEM). By leveraging the flexibility of one-hot encoding and the informative
gradients of signed boundary distances, Hot-Distance enables the use of a
broader range of training data, including datasets with sparse annotations.

Our method addresses the limitations of existing approaches by allowing for
effective learning from non-target crops and providing the capability to
perform gradient-based learning and watershed segmentation. An implementation
of this task is available at github.com/janelia-
cellmap/dacapo/blob/main/dacapo/experiments/tasks/hot_distance_task.py

In our next publication, we will present empirical comparison of Hot-Distance
with traditional one-hot and signed boundary distance loss functions,
examining its potential to improve segmentation performance by utilizing a
more extensive and diverse training dataset. Future work will focus on the
thorough empirical evaluation of Hot-Distance across various datasets and
segmentation tasks, as well as exploring the integration of additional loss
functions to further enhance its performance.

## References

  * Heinrich et al. [2021] Larissa Heinrich, Davis Bennett, David Ackerman, Woohyun Park, John Bogovic, Nils Eckstein, Alyson Petruncio, Jody Clements, Song Pang, C Shan Xu, Jan Funke, Wyatt Korff, Harald F Hess, Jennifer Lippincott-Schwartz, Stephan Saalfeld, Aubrey V Weigel, and COSEM Project Team.  Whole-cell organelle segmentation in volume electron microscopy.  _Nature_ , 599(7883):141–146, November 2021.
  * Heinrich et al. [2018] Larissa Heinrich, Jan Funke, Constantin Pape, Juan Nunez-Iglesias, and Stephan Saalfeld.  Synaptic cleft segmentation in non-isotropic volume electron microscopy of the complete drosophila brain.  In _Medical Image Computing and Computer Assisted Intervention – MICCAI 2018_ , pages 317–325. Springer International Publishing, 2018.
  * Kainz et al. [2015] Philipp Kainz, Martin Urschler, Samuel Schulter, Paul Wohlhart, and Vincent Lepetit.  You should use regression to detect cells.  In _Lecture Notes in Computer Science_ , Lecture notes in computer science, pages 276–283. Springer International Publishing, Cham, 2015.