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The impact of generative articial intelligence on
socioeconomic inequalities and policy making
Valerio Capraro
a,
*, Austin Lentsch
b
, Daron Acemoglu
c
, Selin Akgun
d
, Aisel Akhmedova
d
, Ennio Bilancini
e
,
Jean-François Bonnefon
f
, Pablo Brañas-Garza
g
, Luigi Butera
h
, Karen M. Douglas
j
, Jim A.C. Everett
j
, Gerd Gigerenzer
k
,
Christine Greenhow
d
, Daniel A. Hashimoto
l,m
, Julianne Holt-Lunstad
n
, Jolanda Jetten
o
, Simon Johnson
p
, Werner H. Kunz
q
,
Chiara Longoni
r
, Pete Lunn
s
, Simone Natale
t
, Stefanie Paluch
u
, Iyad Rahwan
v
, Neil Selwyn
w
, Vivek Singh
l
,
Siddharth Suri
x
, Jennifer Sutcliffe
d
, Joe Tomlinson
y
, Sander van der Linden
z
, Paul A. M. Van Lange
aa
, Friederike Wall
ab
,
Jay J. Van Bavel
ac,ad
and Riccardo Viale
ae,af
a
Department of Psychology, University of Milan-Bicocca, Milan 20126, Italy
b
Department of Economics, MIT, Cambridge, MA 02142, USA
c
Institute Professor and Department of Economics, MIT, Cambridge, MA 02142, USA
d
College of Education, Michigan State University, East Lansing, MI 48824, USA
e
IMT School for Advanced Studies Lucca, Lucca 55100, Italy
f
Toulouse School of Economics, Toulouse 31000, France
g
Loyola Behavioral Lab, Loyola Andalucia University, Córdoba 41740, Spain
h
Department of Economics, Copenhagen Business School, Frederiksberg 2000, Denmark
j
School of Psychology, University of Kent, Canterbury CT27NP, UK
k
Max Planck Institute for Human Development, Berlin 14195, Germany
l
Penn Computer Assisted Surgery and Outcomes Laboratory, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia,
PA 19104, USA
m
Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104-6309, USA
n
Department of Psychology and Neuroscience, Brigham Young University, Provo, UT 84602, USA
o
School of Psychology, University of Queensland, St Lucia, QLD 4067, Australia
p
School of Management, MIT Sloan School of Management, Cambridge, MA 02142, USA
q
Department of Marketing, University of Massachusetts Boston, Boston, MA 02125, USA
r
Department of Marketing, Bocconi University, Milan 20136, Italy
s
Behavioural Research Unit, Economic & Social Research Institute, Dublin D02 K138, Ireland
t
Department of Humanities, University of Turin, Turin 10125, Italy
u
Department of Service and Technology Marketing, Aarhus University, Aarhus 8000, Denmark
v
Center for Humans and Machines, Max Planck Institute for Human Development, Berlin 14195, Germany
w
Faculty of Education, Monash University, Clayton VIC 3168, Australia
x
Microsoft Research, Redmond, WA 98502, USA
y
York Law School, University of York, York YO105DD, UK
z
Department of Psychology, University of Cambridge, Cambridge CB 21TN, UK
aa
Department of Experimental and Applied Psychology, Vrije Universiteit, Amsterdam 1081HV, The Netherlands
ab
Department of Management Control and Strategic Management, University of Klagenfurt, Klagenfurt am Wörthersee 9020, Austria
ac
Department of Psychology & Center for Neural Science, New York University, New York, NY 10012, USA
ad
Norwegian School of Economics, Bergen 5045, Norway
ae
CISEPS, University of Milan-Bicocca, Piazza dell’Ateneo Nuovo 1, Milan 20126, Italy
af
Herbert Simon Society, Turin 10122, Italy
*To whom correspondence should be addressed: Email: valerio.capraro@unimib.it
Edited By: Michele Gelfand
Abstract
Generative articial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this
article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and
three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen
existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI
can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the
workplace, it can boost productivity and create new jobs, but the benets will likely be distributed unevenly. In education, it offers
personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen
pre-existing inequalities. In each section, we cover a specic topic, evaluate existing research, identify critical gaps, and recommend
research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section
Competing Interest: The authors declare no competing interest.
Received: December 19, 2023. Accepted: May 3, 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences. This is an Open Access article
distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits
unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
PNAS Nexus, 2024, 3, pgae191
https://doi.org/10.1093/pnasnexus/pgae191
Advance access publication 11 June 2024
Review
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highlighting the role of policymaking to maximize generative AI’s potential to reduce inequalities while mitigating its harmful effects. We
discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom,
observing that each fails to fully confront the socioeconomic challenges we have identied. We propose several concrete policies that
could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary
collaborations to understand and address the complex challenges of generative AI.
Introduction
Advances in generative articial intelligence (AI) represent a shift
in the capability of these systems to solve problems previously
thought unsolvable (1). Techno-optimists predict a utopian future
where machines can perform an ever-increasing number of
tasks—but humans remain in control, the gains from prosperity
are shared throughout society, and we all enjoy lives with less
work and more leisure. In contrast, pessimists forecast a dys-
topian future where machines not only replace humans in the
workplace but also surpass human capability and oversight, de-
stabilize institutions, and destroy livelihoods—and perhaps even
cause the downfall of humanity (2).
Melvin Kranzberg, a prominent scholar in the history of
technology, in a presidential address to his eld, dened
“Kranzberg’s Laws”, the rst of which states that “Technology is
neither good nor bad; nor is it neutral” (3). This principle suggests
that technologies like generative AI will likely have negative and
positive impacts on society, though they are not inherently pre-
destined toward either. In the current article, we outline some
of the more likely positive and negative effects, with the aim of
guiding scholars and policy makers to harness the positive poten-
tial of this new technology while mitigating the costs for individu-
als and society.
Both optimists and pessimists agree that generative AI repre-
sents a qualitative departure from previous automation proc-
esses, such as microelectronics, information technology, and the
Internet. Unlike traditional automation, which primarily focuses
on replicating predened tasks, generative AI introduces the abil-
ity to create new, original output. The implications have the po-
tential to reshape foundational values and skills. For instance,
while generative AI might facilitate written communication, espe-
cially for non-native speakers, it could devalue foundational lan-
guage learning. The incentives to master syntax, vocabulary and
grammar might wane as generative AI begins to exceed the skill
level of humans. This shift reects a broader theme: generative
AI does not merely alter practices but fundamentally transforms
the valuation of knowledge and skills (4).
The effects of generative AI may eventually expand to virtually
every facet of society (5). We begin by discussing the impact of
generative AI in the information domain. Generative AI can dem-
ocratize content creation and access to information but could also
lead to challenges of increased misinformation and eroded trust
in digital content.
In the subsequent sections, we investigate potential down-
stream impacts on socioeconomic inequalities in three key
information-intensive areas: work, education, and healthcare. In
the workplace, generative AI could increase productivity and pro-
mote shared prosperity, especially when used to complement hu-
man efforts and create new well-paid jobs. However, the benets
and costs will likely be distributed unevenly across rm sizes, sec-
tors, and worker demographics. In education, generative AI prom-
ises personalized learning experiences, potentially bridging
educational gaps. However, it also raises concerns about equal ac-
cess to these advanced tools. The health sector could greatly bene-
t from AI’s diagnostic and predictive capabilities, improving
patient outcomes and making healthcare more accessible.
Yet, there is the risk of deepening existing inequalities of care
and access, especially for under-resourced and marginalized
communities. For each of these domains, we explore current re-
search and suggest future directions.
We conclude with an examination of the role of policymaking
in the age of AI. We discuss the pros and cons of the current policy
approaches in the European Union, the United States, and the
United Kingdom, noting that all fall short in addressing the socio-
economic risks that we identify. We argue that policies must be
designed to mitigate the potential problems posed by AI, without
increasing inequality and harm to vulnerable members of society.
We recommend several policies that should be studied empirical-
ly and included in public debate. These include measures to
combat AI-generated misinformation, prevent job market in-
equalities, and bridge the digital divide in education and health-
care. The goal should be to harness the potential of generative
AI in ways that favor human ourishing, striking a balance be-
tween technological advancement and societal well-being.
Impact on (mis)information
Generative AI has the potential to revolutionize the information
domain, impacting areas such as work, education, healthcare,
law, nance, and policy making. One advantage is the ability for
personalization, where AI can tailor content to individual prefer-
ences, enhancing and customizing user experiences. The lan-
guage translation and localization capabilities of AI extend the
reach of content globally, crossing traditional language barriers
and making material accessible to a wide variety of cultural con-
texts and social groups (6). For example, AI aids in making infor-
mation more accessible for individuals with disabilities, by
creating text-alternative formats like audio or simplied summar-
ies. AI may also help automate the fact-checking process, aiding
the spread of accurate information (7, 8).
Concerningly, new generative AI technology and sophisticated
machine learning techniques may also enable companies to col-
lect and deploy excessive amounts of information about individu-
als. This will enable exploitation of consumers’ biases or
vulnerabilities in order to capture more of the consumer surplus
via price discrimination or violations of consumer privacy, leading
to “surveillance capitalism” (9, 10). A dominant model has
emerged from these monopolies, where internet platforms earn
income by optimally marketing digital advertisements (11). This
strategy places a premium on user attention, which has led com-
panies to deploy AI and machine learning techniques to prolong
user engagement, often to the detriment of individual and societal
well-being (12–15). Relatedly, companies with more data possess
an anticompetitive advantage, enabling them to exercise market
power to extract surplus and relax price competition, which can
be detrimental for consumers (9).
Malicious actors can exploit generative AI to create false infor-
mation in ways that convincingly copy the style and content of
human-created text, by synthetically generating text, audio, im-
ages, and videos (“deepfakes”). For instance, malicious generative
AI tools like WormGPT (a ChatGPT alternative for designing
and rening cyber-attack strategies and malware) or PoisonGPT
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(a modied open-source AI model designed to spread misinforma-
tion) show that these tools can be used to accomplish malign aims
and to sabotage further technology development.
Manipulated political images already make up a substantial
portion (∼20%) of visual misinformation on social media (16).
This type of misinformation can be especially common during
elections and intergroup conicts such as the Russo-Ukrainian
and Israel-Gaza wars (17). Additionally, people are largely unable
to tell the difference between AI- and human-generated text (18)
and that AI has been shown to generate more convincing misin-
formation than humans (19) as well as persuasive propaganda
(20). Consequently, there is growing concern that generative
AI may also increase the quantity of misinformation. Indeed,
hundreds of unreliable AI-news websites have popped up (21).
There is currently little legislation preventing the use of deepfakes
in political campaigns, although there are some steps in this dir-
ection. Some US states have introduced legislation prohibiting
their use (22). The EU AI Act (Article 52(3)) does not outlaw deep-
fakes, but at least requires platforms to identify AI-generated
content.
AI-assisted misinformation can spread rapidly on social media
and micro-targeting people with deepfakes can inuence their at-
titudes toward politicians (e.g. (23)). GPT can also automate micro-
targeting in a way that makes it more persuasive than standard
nonpersonalized ads (24). The possibility to create information
that is personalized or targeted to specic individuals and groups
is likely to increase, especially during elections (25). Politicians, in-
cluding Republican presidential candidate Ron DeSantis, have al-
ready started using deepfakes in their political campaigns, such as
fake images of Donald Trump hugging Anthony Fauci (26). The
2024 US Presidential election has been dubbed “The Deepfake
Election” (27).
This increase in misinformation may have signicant social
consequences. Conspiracy theories and misinformation can con-
tribute to attitude polarization (28) and undermine trust (17).
Moreover, political conspiracy theories and misinformation can
affect voting decisions, health-related conspiracy theories can in-
uence people’s medical choices (e.g. vaccination), and misinfor-
mation and conspiracy theories can fuel conict between groups
(29, 30). While some people simply ignore online misinformation
(31), this content is likely to penetrate specic groups, especially
since AI may help automate the micro-targeting process in which
thousands of persuasive messages can now be generated easily at
scale (24). For example, there is evidence that Trump voters were
more susceptible to misinformation during the 2016 presidential
election (32).
Therefore, regulation and interventions are urgently needed to
limit the diffusion of AI-generated misinformation. Simply warn-
ing people of deepfakes or including a tag clarifying whether a
piece of content is AI-generated might backre, as such tags
may reduce the believability of true content as well (17, 33). In
the realm of human-generated misinformation on social media,
psychological interventions based on accuracy-salience and edu-
cational interventions based on inoculation theory improve the
quality of information shared. For example, making the concept
of accuracy salient can reduce the sharing of fake news, without
adversely affecting the dissemination of accurate news (34).
Moreover, endorsing accuracy not only decreases the sharing of
false news but also increases the sharing of true news (35).
However, these interventions have typically modest effects com-
pared to alternative strategies (36).
Inoculation theory or “prebunking” is a preemptive approach
to countering misinformation that follows the vaccination
analogy (37). Several inoculation games and videos have been
developed to expose subjects to controlled (weakened) doses of
misinformation along with tools on how to spot it and these activ-
ities make them better at detecting online manipulation (38).
Similarly, in a eld study on YouTube, videos containing micro-
doses of common misinformation techniques increased discern-
ment of online manipulation tactics (39). Because prebunking
often works better than debunking (40), future work could adapt
these techniques to AI-generated news (41).
One concern, however, is that interventions based on accuracy-
salience and inoculation may be most effective for easily discern-
ible misinformation. Unfortunately, generative AI makes
misinformation more subtle and harder to discern (18), which
may necessitate a new toolbox of interventions, specically de-
signed to counteract (visual) AI-generated misinformation.
Moreover, generative AI could lead to entirely new challenges,
such as tackling misinformation disseminated via one-to-one per-
sonalized communications (e.g. through bots). This further high-
lights the urgency to adapt existing or develop a new set of
intervention strategies (42). New policies on social media
platforms and effective regulation are likely needed to address
this issue at scale rather than relying too heavily on subtle inter-
ventions. An intriguing direction would be to explore how genera-
tive AI itself could be leveraged to combat misinformation. For
instance, one study found that engaging in dialog with AI reduces
conspiracy beliefs among conspiracy believers (43).
Even in the absence of malicious actors, the most advanced AI
systems are known to “hallucinate” false information in a very
realistic manner (1). These hallucinations may induce complex
social dynamics, like self-fullling prophecies, where an initially
false prediction becomes true just because someone—e.g. a gen-
erative AI system—asserts that it will become true (44). In this
sense, AI may produce prophecies that could “take a life for their
own” (45). For example, automated scoring systems that predict
the likelihood of default on debt repayment may contribute to
(or even cause) credit risk.
Another important area of concern is that individuals may
not know whether they are interacting with a person or a
machine (46). This is increasingly likely to be the case when
people engage online with businesses and public services. If
they believe, rightly or wrongly, that they are interacting with a
machine, their behavior is likely to change (47). For instance,
while AI can generate responses that make people feel heard by
offering emotional support, people feel more heard when they be-
lieve a response comes from a human (48). The mere knowledge
people are interacting with a machine can change their
experience.
Human behavior tends to become more selsh in human–
machine interactions because reciprocation—a vital factor in sus-
taining prosocial behavior—is not maintained as consistently as
in human–human interactions (49, 50). Prosocial behavior de-
pends on people’s beliefs about the relationship between the ma-
chine and the humans behind it (51). When interacting with a
machine, people respond less emotionally, feeling less guilt about
being ungenerous (52). They become more likely to be dishonest in
pursuit of monetary rewards (53). An outstanding question con-
cerns whether similar slippage from ethical standards occurs
not only among people interacting with a machine but also among
those who delegate to the machine (54).
One overlooked implication is the impact of generative AI on
the plurality of information available on the web. Companies in-
cluding Microsoft and Google have envisioned integrating large
language models into their search engines, but the implications
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of this move have only started to be explored (55). Among the most
signicant implications is users’ access to information. Search en-
gines powered by generative AI may restrict the plurality of infor-
mation available on the web. When users input a query into the
current version of Google Search they are pointed to a plurality
of sources. Although users tend to select among the rst results
returned by the search engine (56), the interface enables them
to browse many alternative results. The same input directed to
a search engine powered by generative AI will provide an extra
layer of mediation likely to provide a much more limited amount
of source information, unless specic design features are included
to counteract this (57).
In addition to reducing access to information, generative AI
may also threaten the quality and availability of online informa-
tion. The already-pervasive issue of bot accounts may be exacer-
bated by new generative technologies, which can assist in coding a
multiplicity of these bots as well as providing text content for the
bots to post (58). These tools could also be used to generate con-
tent optimized for search engines en masse, a useful tactic for busi-
nesses to “poach” trafc from competitors’ websites (e.g. (59)).
This is a problem because current generative AI tools essentially
provide an “average” response to a particular question, and these
models are trained largely on text data collected from the inter-
net; therefore, if the practice of generating content optimized for
search engines at massive scale becomes common practice,
both generative AI tools and online information may crater into
an average of averages, lacking true insight, creativity, or novel
ideas. At the very least, it could make useful contributions difcult
to identify within a sea of mediocre machine-generated “average”
content.
An analogous concern is that common message boards and
websites for knowledge sharing (e.g. Stack Overow) have experi-
enced both a reduction in questions posted—especially the basic
questions that ChatGPT does well at answering—and an increase
in question responses, perhaps due to writing aid from tools like
ChatGPT (60). Though these Q&A sites require competent subject-
matter experts to provide insights and suggestions, they also re-
quire neophytes to ask those questions in the rst place.
Reduced engagement by novice users not only has effects on the
continued usefulness of these websites to aggregate and share
knowledge but also for innovation and creativity that may rely
on content from these platforms as input.
Many of these challenges require governmental regulation. We
will discuss specic policy recommendations in the last section.
However, it is important to recognize that organizations also
play a signicant role. Organizations often face trade-offs be-
tween achieving their protability goals and adhering to ethical
practices. One way to view these trade-offs is through a utilitarian
perspective, weighing the benets and costs. For instance, privacy
experts have developed the “privacy calculus” to address such
trade-offs from the consumer perspective (61). Similarly, Wirtz
et al. (62) argue that service rms engage in a “corporate digital re-
sponsibility calculus” and weigh the benets and costs of follow-
ing ethical principles to determine their level of engagement in
digital responsibility. Therefore, we might expect service rms
to engage in responsible practices when the benets outweigh
the costs. Digital responsibility does present benets. By adopting
responsible practices, corporations can build trust with their cus-
tomers and stakeholders, differentiate themselves from competi-
tors, and enhance their reputation. However, if the costs of
adopting responsible practices is too high, rms may be less likely
to engage in good practices and regulatory enforcement may be
necessary (62).
In conclusion, while generative AI has the potential to expand
access to and content of information, it also raises signicant
challenges such as market anticompetitive advantages,
data misuse, data poisoning, misinformation proliferation,
and altered human–machine interactions, all of which necessi-
tate careful consideration and targeted research. Table 1summa-
rizes the main directions of future research, along with one
specic research question for each direction, potential design,
and theoretical trade-off that complicate the derivation of hy-
potheses a priori. Table S1 extends this table to three specic re-
search questions for each direction. This list is not meant to be
exhaustive but serves as an initial guide for subsequent
investigations.
Impact on work
Previous waves of digital technologies have contributed to in-
creased inequality. Some of these technologies, like personal com-
puters, have been complementary mostly to educated workers
(63, 64), while others, like industrial robots, have been used to
automate repetitive or systematic tasks that are often performed
by less-educated workers (65, 66). Together, the upside for
more-educated workers and downside for less-educated workers
have magnied the distributional consequences of technological
innovation, highlighting what is sometimes called “Skill-Biased
Technological Change” (67).
The current trend in AI emphasizes automation. While some
amount of this is unavoidable, the displacement of labor by
“so-so technologies” (e.g. self-checkout kiosks or automated
phone systems) that offer little or no productivity gain, along
with diminished worker voice due to intensied monitoring and
surveillance, can be harmful to long-run productivity and other
social goals like job satisfaction (65, 68). Although new technolo-
gies can boost productivity (69), the gains have often fallen below
expectations, especially when the focus has been on replacing
work instead of augmenting worker capabilities or developing
new ones (11, 70).
New technologies like AI should be oriented not so much to-
ward replacing human problem-solving abilities, but rather to-
ward enhancing them in a symbiotic relationship where
machines are designed to complement human capabilities and
humans can compensate for the weaknesses of machines (71).
This “pro-worker” or “human-complementary” path could con-
tribute more to productivity growth and could help reduce
economic inequality. The question we is whether AI will acceler-
ate the existing trend of automation without the offsetting
force of good-job creation—particularly for noncollege educated
workers—or whether it will instead introduce new value-adding
tasks and well-paying jobs for workers with diverse skill sets
and educational backgrounds.
There is cause for optimism: AI can complement workers by
making them more efcient, helping them to produce higher
quality work, or enabling them to take on new value-adding tasks
(72–74). Brynjolfsson et al. (75), for instance, consider the stag-
gered implementation of a chat assistant by a Fortune 500 soft-
ware company that provides business process software. The
chat assistant monitored customer service chats and proposed
real-time response suggestions to customer service agents.
Less-skilled or inexperienced workers resolved around 34% more
issues per hour, with average improvement across all workers
measuring about 14%. Agents using the tool with only 2 months
of tenure performed as well as those without the tool who had
more than 6 months of tenure.
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Another study examined the impact of GPT-4 access on com-
plex knowledge-intensive tasks. AI users were more productive
and produced higher quality work. However, for tasks beyond
the capabilities of GPT-4—specically, tasks that involve imper-
fect information or omitted data, which require cross-referencing
resources and leveraging experience-gained intuition—AI usage
resulted in fewer correct solutions. Consultants with
below-average performance improved by 43% with AI, while those
above average improved by 17% (76).
Similar patterns have been observed in other studies.
For instance, Peng et al. (77) conducted a controlled experiment
with GitHub Copilot, an AI-based programming assistant.
Programmers with access to the AI copilot completed a task in
71 min on average, less than half the time of the control group’s
161 min. The AI assistant provided the biggest boost to
less-experienced and older programmers, as well as those coding
more hours daily. Similarly, people with access to ChatGPT com-
pleted a writing task faster and produced higher quality work
(78). Again, this reduced worker inequality by beneting lower-
ability workers more; moreover, it led to higher job satisfaction
and self-efcacy.
These studies underscore the potential of generative AI to dis-
proportionately boost productivity for workers with less experi-
ence or skill. This differs from previous technologies. Instead of
Table 1. Summary of the main research directions on the impact of generative AI on information.
Future research directions
Research area Specic question Potential design Trade-off
Investigate how AI can be used to
make information more
accessible, especially for
individuals with disabilities.
Can AI-based summarization tools
improve information
accessibility for individuals with
cognitive disabilities by
simplifying complex texts?
Evaluate the comprehension of
complex news articles by
individuals with cognitive
disabilities after using AI-based
summarization tools, compared
with individuals not using these
tools.
Summarization could make content
more accessible, but
oversimplication might omit
critical details, challenging the
balance between accessibility and
content accuracy.
Understand how the largest rms
could monopolize the future of
AI; nd ways for smaller and
innovative rms to effectively
compete with those largest
players.
Does open-source AI decrease the
risk of monopolization by large
rms?
Compare the growth and success
rates of rms that contribute to or
use open-source AI vs. those
relying on proprietary solutions.
Open-source could democratize AI
development, but the capacity of
small rms to benet from it may
be smaller compared to large
rms’, affecting the potential of
such measures to reduce the
competitive gap.
Explore regulatory measures to
prevent misuse or inappropriate
access to data by AI systems.
Can a standardized transparency
protocol prevent data misuse
and privacy breaches?
Develop a standardized
transparency protocol and test its
impact on the frequency of data
misuse and privacy breaches.
Measure also users’ trust in the
system and data sharing.
Increased transparency may
decrease data misuse and privacy
breaches, but the difculty of
transparency protocols may
actually decrease public trust and
data sharing.
Investigate strategies to identify
and limit the spread of
misinformation generated by AI.
Can dialoging with gen AI reduce
beliefs in misinformation?
Compare misinformation beliefs
between participants engaging
with an AI-tool designed to
counter misinformation and those
interacting with a neutral AI.
AI’s capabilities for personalization,
linguistic uency, and logical
reasoning can diminish
misinformation beliefs, but the
risk of hallucinations could
generate new misinformation.
Explore ways to design AI systems
that support cooperative and
ethical behavior in human–
machine interactions.
Do humans become more
unethical when they can
delegate decisions to AI
compared to other humans?
Subjects make morally ambiguous
decisions, with the option to
delegate these decisions either to
AI or to another human, observing
changes in their ethical standards.
The emotional detachment of AI
may reduce moral responsibility,
but the potential for AI to
document and expose decisions
might increase personal
accountability.
Examine how AI-enhanced search
engines can be designed to
preserve user autonomy and
plurality of information.
Does making the data sources of
AI-enhanced search engines
transparent inuence user
engagement with alternative
information sources?
Disclose the data sources for AI
algorithms in search engines and
assess whether users seek out
additional, alternative sources of
information as a result.
Transparency about data sources
might encourage users to seek
diverse information, but could
also lead to information overload.
Consider how the proliferation of
AI-generated content could
lower the quality of online
information and ensure that
human users can continue to
contribute new knowledge.
How does the ratio of AI-generated
to human-generated content
affect content diversity on online
forums?
Manipulate the ratio of AI-generated
to human-generated content in an
online forum environment. Survey
users on perceived content
diversity and quality.
Higher ratios of AI content could
enhance content availability, but
may also homogenize the content
or lead to mediocre or
over-creation of banal, “average”
content.
Investigate the role of Corporate
Digital Responsibility and its
implementation challenges
What are the main barriers to
implementing effective digital
responsibility strategies in
organizations?
Development of organizational
strategies to implement a general
Corporate Digital Responsibility
framework.
Many organizations might not be
aware of the role and importance
of corporate digital responsibility
strategies, which might cause
over-regulation.
For each relatively broad research area, we propose three specic research questions, along with potential experimental designs. We also identify an underlying
theoretical trade-off that complicates the derivation of a priori hypotheses. This list is not exhaustive but rather provides examples of the kinds of questions future
research should aim to address.
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mostly beneting more-skilled workers, generative AI tools seem
to exhibit a worker-complementary “inverse skill-bias,” benet-
ting less-skilled workers much more than highly skilled workers.
An example discussed further in the healthcare section nds
that some physicians perform more poorly when they use AI tools
to support diagnostic decisions, compared to their performance
without AI usage (79). This suggests that gains from integrating
generative AI into medicine could be better targeted to nurses,
medical technicians, or healthcare administrators (80). These
workers have the requisite baseline knowledge to conduct critical
components of care delivery, but perhaps could be empowered to
do alleviate the “enormous administrative burdens” that current-
ly exacerbate physician shortage (81). These AI tools and practical
applications are still in their infancy. Yet, if these trends continue,
it is possible that generative AI could reverse the income and job
market inequalities and “rebuild the middle class” in advanced
economies (82).
Generative AI could also reduce barriers to entry in the digital
economy. For example, its translation capabilities can help over-
come language barriers. This increased accessibility, in conjunc-
tion with trends toward diminishing geographic barriers, could
have a compounding positive effect. Accordingly, there has been
a surge of interest in remote-enabled digital economy jobs, espe-
cially in rural areas (83). Yet, these benets have mostly favored
well developed countries. While generative AI could also offer ex-
panded opportunities to countries in the “Global South”, it is un-
likely to have much direct impact in the near term due to
insufcient investment in prerequisite digital infrastructure, local
researchers, and broader digital skills training (84).
One strength of generative AI is its ability to parse and aggre-
gate enormous amounts of information. This capability can
equalize access to information and lower research costs by simpli-
fying online search tasks. If a user wants to accomplish a complex
task with a traditional search engine, they have to break that task
into pieces, issue search queries for each piece, read the web pages
returned by the search engine, assess the representativeness of
their gathered information, and then aggregate the results to
solve the problem. Generative search engines, on the other
hand, can aggregate this information and return it to the user, re-
quiring less bandwidth and fewer trips between the user and the
system which would be helpful in lower resource environments.
In addition to the time and cost savings, these tools could com-
pensate for expertise by identifying trustworthy resources and ex-
tracting the consensus on any topic by simultaneously
considering more information than human operators can retain.
This approach could help users and businesses in low-resource
settings access information that has traditionally been available
only in high-resource environments.
However, there are also ways in which AI might exacerbate in-
equalities in the workplace. One concern is differential access to
these tools. The most widely available and accessible generative
AI platforms still require additional technical inputs (e.g. internet
access and internet-enabled devices) as well as training to opti-
mize performance. Industries, rms, and workers that have not
yet integrated the prerequisite technologies will struggle to take
advantage of the expanded capabilities and consequent product-
ivity and earnings upsides, likely falling (further) behind well-
resourced competitors or coworkers.
The role of rm behavior and social context matters. For ex-
ample, while the introduction of generative AI tools gives more
of a boost to less-skilled workers, this equalizing force could be
a way for workers to increase their earnings potential if compen-
sation is tied to capability. Instead, if rms exploit the higher
interchangeability between workers (“why hire an expert copy-
writer if a less-skilled writer with an AI chatbot can do the same
level of work?”) these wage gains may never be realized.
AI will likely have outsized impacts on US workers with
Bachelors’ or Associates’ degrees, compared to higher or lower
levels of education (85). This effect could compound over time: if
generative AI tools commodify expertise and reduce the returns to
specialized skills, workers may no longer spend the time or re-
sources to acquire greater levels of expertise, leading to lower lev-
els of worker skill and over-reliance on outsourcing to generative
tools. These effects could cause greater competition at the
(now larger) lower end of the skill distribution, further depressing
wages. There could be further downsides to productivity if
nonautomatable job tasks would benet from workers having ac-
quired the sort of foundational knowledge that is now
disincentivized.
Governments may play an important role in mitigating the risk
of increased inequality and maximizing the productivity potential
of new generative AI tools. Explicit policy suggestions are post-
poned to the “Policymaking in the age of articial intelligence” sec-
tion (see also Table 5). Table 2reports a summary of the main
research directions on the impacts of generative AI in the work-
place, along with a set of specic example questions. See
Table S2 for an extended version.
Impact on education
The integration of generative AI in education represents a con-
tinuation of the technological evolution that began with Massive
Open Online Courses and similar initiatives. Massive courses
have increased access to education to some degree (86).
However, they have often fallen short of their anticipated trans-
formative impact for various reasons, including minimum learn-
ing by doing and lack of personal support (87). Generative AI
brings a distinctively novel element to educational technology:
the role of chatbot tutors, which interact with students to foster
skills ranging from prompt engineering to critical thinking and
creative ideation. This shift towards using technology as a dynam-
ic partner paves the way to truly skill-adaptive and personalized
teaching and on-demand student guidance and support that
does not require continuous, intensive investment from educa-
tors to repackage content to best meet students’ needs. These
uses could be particularly effective in large class settings, with sig-
nicant opportunity to scale-up implementation beyond the cap-
abilities of traditional educational practices. Consequently,
generative AI could bridge complex and persistent educational
gaps.
A review of AI applications in education identied several use
cases that produced higher test scores when students used per-
sonalized learning systems (88). These systems, unlike traditional
approaches like static worksheets with standardized questions,
detect areas where students lack foundational understanding by
adapting educational resources and tools to foster their develop-
ment. Furthermore, assessment algorithms can expedite grading
of written assessments, which supports students by offering time-
ly feedback that can be applied immediately. Students themselves
perceive AI as potentially benecial to their education. College
students reported that generative AI provided personalized learn-
ing, supported their writing and brainstorming, and assisted with
research and analysis (89). However, students also expressed con-
cerns about the accuracy, privacy, and ethical implications of gen-
erative AI tools—including how this technology could adversely
impact their personal development and career prospects.
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Educational uses of generative AI pose several other chal-
lenges. One is the perpetuation of biases and discrimination, po-
tentially reinforcing racial or gender-based stereotypes during
personalized learning, automated scoring, and admission proc-
esses (88, 90). The data used to train AI models could suffer
from bias, if those data are based on past human decision making
(a notoriously biased process). An example is the translation bias
observed in tools like Google Translate, where gender stereotypes
are inadvertently perpetuated in language translations.
Translating the phrase “she/he is a nurse” from Turkish (which
is “genderless”) to English (which is “gendered”) yielded the femin-
ine form (i.e. “she is a nurse”), while the phrase “she/he is a doctor”
yielded the masculine form (i.e. “he is a doctor” (91)). Failing to ac-
count for these biases could amplify inequalities and injustices,
specically towards historically marginalized groups.
Given that human educators are susceptible to biases and dis-
crimination, AI systems offer a theoretical advantage: they could
be engineered to exhibit less bias. A signicant benet of AI is the
ability to audit and address biases within educational systems, a
process that proves difcult, if not impossible, with human biases.
However, simply introducing slightly less discriminatory tech-
nologies into classrooms is not a substitute for the goal of remov-
ing discrimination from school (92). Moreover, AI systems should
be designed with sufcient transparency for users to monitor for
and identify potential biases to ensure that these tools effectively
serve their intended purposes and reect the interests of key
stakeholders, including students, teachers, and parents (93).
Group-based inequalities may widen because of varying levels
of engagement with generative AI tools. For instance, a study re-
vealed that female students report using ChatGPT less frequently
than their male counterparts (94). This disparity in technology us-
age could not only have immediate effects on academic achieve-
ment but also contribute to future gender gap in the workforce.
Therefore, efforts should be made to ensure the benets of gen-
erative AI tools are fairly distributed across all student segments.
It is unclear if Generative AI will alleviate or place increased
burdens on teachers. In contrast to the idea that AI tools relieve
teachers of repetitive and onerous work, there is growing concern
Table 2. Summary of the main research directions on the impact of generative AI in workplace environments, along with specic
example questions and corresponding experimental design and theoretical trade-offs.
Future research directions
Research area Specic question Potential design Trade-off
Investigate how AI can be designed
and implemented to augment
human skills and increase
productivity, rather than to
simply replace workers and
forego the long-run productivity
upsides of maximizing workers’
contributions to production.
How do organizations balance
automating knowledge tasks vs.
hiring more knowledge workers
for efciency and innovation?
Analyze organizational decisions,
assessing the ratio of investment
in automation technologies to the
recruitment of knowledge
workers.
Automation offers cost efciency
and 24/7 productivity, but the
unique insights brought by
human knowledge workers
present a compelling case for
augmentation over full
automation.
Examine how AI can facilitate more
access to economic
opportunities, particularly
through reducing
language-related barriers and
promoting remote work
technologies that can
democratize access to the digital
economy.
What role does AI play in enabling
marginalized groups, including
countries in the Global South, to
access remote work
opportunities in the digital
economy?
Analyze employment trends and
outcomes in marginalized groups
before and after introducing
AI-enabled remote work
platforms.
AI could provide anonymized job
matching, enhancing access, but
restrictions on technology use
could negate these benets.
Conduct long-term studies to
monitor the evolving impact of AI
on the workforce, capturing both
the immediate and delayed
effects on work across
educational and occupational
strata.
How does continuous use of AI tools
impact job satisfaction and
employee burnout?
Track job satisfaction and burnout
rates among employees using AI
tools vs. those using traditional
methods over multiple years.
AI tools might boost innovation and
creativity, but could also lead to
higher expectations and
workload, decreasing
satisfaction, and increasing
burnout.
Explore how AI can be utilized in
educational and training
programs to encourage basic
competency with generative AI
tools and better-equip workers in
vulnerable job sectors in
anticipation of labor market
changes.
How does intergenerational
collaboration within teams,
specically focused to the
exchange of AI tool knowledge,
impact team performance,
inclusivity perceptions, and
psychological well-being?
Create diverse teams to assess the
impact of intergenerational AI
knowledge-sharing on
performance, inclusivity
perception, and well-being.
Intergenerational knowledge
sharing might improve team
performance, inclusivity
perceptions, and well-being, but
it could also introduce challenges
such as resistance to change from
senior members or frustration
among juniors, with negative
effects on the outcome variables.
Research labor laws, taxation
policies, and social support
systems that could support
workers displaced or
disadvantaged by AI.
How do worker retraining and job
replacement programs impact
the economic stability of workers
displaced by AI?
Incorporate AI-skills into pilot
worker retraining programs in
regions with high rates of
AI-driven displacement,
comparing against control groups
in similar regions with alternative,
non-AI worker retraining.
AI-centric worker retraining could
enable workers to be more agile in
nding re-employment in the
event of job displacement, but
this may not be successful for all
workers, particularly those later
in their careers or with fewer
pre-existing technical skills.
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that teachers must engage in additional tasks “behind the scenes”
(e.g. curating and ltering content, monitoring student–AI inter-
actions, providing technical support) to ensure that AI tools are
able to function in complex classroom settings (95). This could ex-
acerbate a generational divide among educators, as younger
teachers may be more adept with new technology than older
teachers. Furthermore, there could be unintended consequences
of generative AI on student learning—for example, if students
become overly reliant on support from AI tools, this could
undermine their capacity to work or think independently.
Questions also arise about the accuracy of AI-generated content
and the new skills that students must acquire to work effectively
with AI systems, such as the ability to evaluate AI-generated
content.
The current debate about the role of generative AI, from pri-
mary schools to universities, revolves around whether generative
AI should be banned, permitted under only some cases, or allowed
as assistance for teachers and students. For instance, the
New York City education department and Chinese universities
have banned generative AI (96, 97), while the Berlin universities
recommended its use in certain scenarios. A growing literature
recommends the use of generative AI for teacher and student as-
sistance within the traditional curricula (e.g., (98).
We argue that these approaches are limited in vision. A more
forward-thinking approach would involve a curricular revolution
to redene the skills and competencies necessary to effectively
utilize generative AI. Calculators did not remove the need for stu-
dents to learn the properties of algebra and develop mathematical
reasoning. Similarly, the internet did not eliminate the need for
careful research and fact checking; in fact, it increased this
need, as online information is frequently incorrect or incomplete.
In the same vein, generative AI will not eliminate the need to learn
effective thought organization, writing, and critical thinking skills.
Therefore, curricula must teach how to successfully describe
and share ideas, both with and without assistance from genera-
tive AI. In addition, they need to emphasize the development of
critical-thinking skills, fact-checking abilities, an understanding
of how generative AI tools function, and appropriate rules of inter-
action—including by refraining from anthropomorphizing (and
thus misunderstanding) these tools (99).
More specically, the text-production abilities of generative AI
present an opportunity to teach students critical thinking. This
will enable them to evaluate the argument and structure of the
generated text and to write intelligent prompts for generative
AI. This skill should be recognized and assessed by educators.
The output of generative AI is much more variable than other edu-
cational technologies (e.g. calculators); therefore, developing
these critical thinking abilities and prompt-engineering skills is
fundamental.
Another crucial skill is the ability to fact-check generative AI
outputs. Fact-checking skills are not taught sufciently in schools.
For instance, among more than 3,000 US high school students and
undergraduates, 96% did not know how to evaluate the trust-
worthiness of websites (100). These fact-checking abilities include
smart heuristics such as lateral reading; i.e. the practice of navi-
gating away from an unfamiliar website to verify the reliability
of its information by consulting other external sources (101). A
toolbox of similar fact-checking heuristics needs to be developed
or remediated for AI-generated content.
Lastly, understanding the nature of large language models,
which are statistical machines that calculate correlations be-
tween words, is essential. Only in this way can students under-
stand the potential and limits of generative AI, rather than
assuming that contemporary generative AI can “think” or “com-
prehend” like humans.
The adaptation of curricula is challenging, but essential for the
future of education. Without such changes, teachers and students
may use generative AI merely as an automated assistance tool.
This would forego the opportunity to develop higher-order cogni-
tive skills, such as critical judgment and fact checking, that gen-
erative AI itself cannot reliably perform. The result would be a
likely decline in higher-order cognitive skills, especially in seg-
ments of the population that will use these tools in a more mech-
anical, less analytical manner. The role of governments in
integrating generative AI into the education sector is crucial. We
will discuss potential policy recommendations in the nal section
(see Table 5). Table 3summarizes the main research directions,
along with specic research questions. We refer to Table S3 for
an extended list of research questions.
Impact on healthcare
Recent advances in AI techniques can democratize healthcare by
making efcacious medical care more accessible and affordable.
This is often achieved via augmenting human capacities and re-
ducing workload. AI can support clinicians with diagnosis, screen-
ing, prognosis, and triaging, alleviating the burden on health
practitioners and giving them the “gift of time” (102). For instance,
a review of workplace burnout among healthcare providers iden-
tied electronic health record systems as a cause of increased
stress due to insufcient documentation time, a high volume of
patient communications, and negative perceptions by providers
(103). In response, generative AI models may aid in the completion
of electronic health record-related tasks, reducing healthcare pro-
fessionals’ administrative demands (104).
Using AI systems as clinician copilots could also improve diag-
nostic accuracy and potentially curb biases. For instance, in a
randomized test intervention, physicians answered questions
around triage, risk, and treatment in chest pain evaluation scen-
arios, and then reconsidered these answers after receiving advice
generated by GPT-4 (105). Not only were clinicians willing to heed
the advice of the generative AI chatbot, but doing so was also as-
sociated with improved accuracy of diagnoses and a reduction in
gender and race bias in decisions.
AI systems can also aid in “medical visual question answer-
ing”—analyzing medical images (like X-rays or MRI scans) and
providing answers to specic questions about these images, typic-
ally by leveraging advanced image recognition and AI algorithms
(106). GPT-4 demonstrates reasonable diagnostic accuracy in sim-
ple cases and can answer questions on standardized medical ex-
ams, though it struggles with diagnostically complex prompts
(107).
AI systems could also assist healthcare providers by analyzing
and interpreting multimodal clinical data (e.g. photos, radiology
images, and surgical videos) to provide relevant information to
clinicians (108). In one study, endoscopists reviewed colonoscopy
videos with and without AI assistance. Their decisions were inu-
enced by AI, particularly when its advice was correct. The integra-
tion of human and AI judgment led to superior performance
compared to either alone, highlighting effective human-AI collab-
oration dynamics in medical decision-making (109). Alas, diag-
nostic performance of some expert physicians may not be
improved by AI—and in fact may cause incorrect diagnoses in sit-
uations that otherwise would have been correctly assessed (79).
Further research is needed to identify under what circumstances
clinicians should or should not heed the advice of generative AI,
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and for which clinicians and medical contexts these tools enhance
or hinder patient outcomes.
Generative AI could also benet patients. For instance, AI sys-
tems could enable patients to manage their health more pro-
actively through applications that patients can access outside of
clinical settings. ChatGPT, for instance, has reasonable accuracy
in answering common myths about cancer (110). Research found
that people trusted ChatGPT’s answers to low-risk medical ques-
tions, though trust reportedly varied for questions with greater
medical complexity (111). Furthermore, ChatGPT’s answers to
medical questions posted on Reddit’s r/AskDocs were rated as
higher quality and more empathetic than those of physicians
79% of the time (112).
Conversational agents based on generative AI can also provide
greater access to medical advice and simplify medical jargon. This
may have positive downstream effects on inequality. Being part of
a stigmatized group affects people’s engagement and utilization
of healthcare services. For example, when contextual cues made
racial stereotypes salient, Black women were more likely to feel
anxious in a healthcare setting than their White counterparts
(113). Health professionals are also biased in their treatment of
higher-weight patients (114). As a result, members of stigmatized
minority groups are less likely to listen to, or trust, doctors who
they perceive as outgroup members (115). It is possible that inter-
actions between members of stigmatized groups and the health-
care system might be more positive when some decisions are
AI-mediated because a patient’s stigmatized status may not be
as salient as it would be in human-to-human interaction. This
suggests that members of stigmatized groups could become
more likely to engage with AI-led healthcare because they worry
less about group- or identity-based factors affecting their treat-
ment options.
However, there are several important steps required to ensure
that this promise comes to fruition. Pre-existing societal biases are
often baked into healthcare data—especially when that data con-
sist of human clinicians’ decisions and the socioeconomic factors
that inuence patients’ presentation to healthcare facilities. This
can make human biases difcult for an AI program trained on that
data to overcome. Furthermore, many of the diagnostic criteria
and treatment algorithms used in healthcare are also subject to
bias, which can drive unequal health outcomes for underserved
populations. Although these biases are pervasive in training data-
sets, there is cause for optimism. Pierson et al. (116) proposed a
model, trained on knee X-rays that is nearly ve times as effective
at predicting knee pain in osteoarthritic patients, compared to the
traditional Kellgren–Lawrence grading system, which overlooks
racial disparities. Obermeyer et al. (117) found that a healthcare
resource-allocation algorithm favored White over Black patients
with the same health risk due to its cost-based criteria, but adjust-
ing the focus to patient health eliminated this racial bias.
Such promises in overcoming dataset issues will require up-
dates to laws and regulations governing healthcare data as well
as the implementation of incentives to shift the culture of health-
care facilities to promote interoperability and data sharing. While
hospitals are developing siloed versions of generative models to
facilitate care while preserving privacy, the true potential of AI
models will likely rely on methods that leverage the value of large,
generalizable datasets.
Table 3. Summary of the main research directions on the impact of generative AI on education, along with specic research questions,
with corresponding experimental design and theoretical trade-offs.
Future research directions
Research area Specic question Potential design Trade-off
Examine how generative AI can be
effectively used for personalized
learning.
How does the use of generative AI
for personalized feedback in
essay writing impact students’
writing prociency over a
semester?
Compare students in classes
receiving personalized AI
feedback with those receiving
standard teacher feedback. Assess
improvements in writing
prociency.
Personalized AI feedback could offer
more tailored and immediate
improvements but may lack the
psychological understanding and
motivational impact of human
feedback.
Investigate how curricula can be
redesigned to include generative
AI as a tool for enhancing learning
while also teaching students to
critically engage with and
understand this technology.
How does integrating generative
AI in science curricula affect
students’ understanding of
complex concepts?
Implement generative AI in a subset
of science classes, comparing
students’ performance on concept
understanding to those in classes
without AI tools.
Generative AI may enhance concept
understanding through
personalized learning, but the
potential for over-reliance on AI
could hinder independent critical
thinking skills.
Study effective training methods for
teachers to integrate AI tools into
their teaching practices and
identify the additional support
required to manage these
technologies in the classroom.
Can peer mentoring programs
increase teachers’ condence
in using generative AI tools?
Implement a peer mentoring
program where AI-experienced
teachers mentor those less
familiar, and assess changes in
condence and usage rates of AI
tools in teaching.
Peer mentoring could provide useful
support and encouragement, but
mismatches in teaching styles
might limit the program’s
effectiveness and even reduce
condence.
Explore strategies to ensure that use
of generative AI leads to a
diversity of educational
experiences and outcomes
How does the educational use of
generative AI affect students’
creativity?
Study three groups of students: one
using generative AI with educator
support for creativity, one using AI
independently, and the other not
using AI. Assess the creativity of
their outputs.
Generative AI can expand students’
creativity with proper guidance but
might reduce originality and
insight if used without educational
support.
Evaluate the long-term impacts of
generative AI on student learning,
teacher workloads, and
educational outcomes.
Does the use of generative AI in
lesson planning reduce
teachers’ preparation time?
Track lesson preparation time for
teachers using generative AI tools
vs. traditional planning methods
over a semester.
AI tools could streamline the lesson
planning process, but the initial
learning curve and adjustments to
AI suggestion might offset time
savings.
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Benets aside, patients, medical providers, and those man-
aging healthcare systems may be hesitant to adopt AI due to sev-
eral psychological barriers. In fact, the impact of AI on clinical
practice has been limited despite the growing number of AI tools
(118). One key factor is public trust in AI technologies in health-
care (119). For instance, patients may resist adoption because of
misperceptions about AI, such as the belief that AI cannot account
for a person’s uniqueness as well as a human doctor (120), or be-
cause of difculty in holding AI accountable for mistakes (121).
Another factor implicated in adoption hesitancy is the contrast
between AI’s opaqueness and the illusory perception that human
decision-making is more transparent than AI. Decisions made by hu-
man physicians or AI are probably equally unobservable to a patient
—but because patients feel that they can understand the decision-
making as explained by human providers, they ultimately penalize
and resist the clinical use of AI (122). The most recent versions of
AI tools may be less susceptible to concerns about AI’s inscrutability,
since the iterative nature of newer generative AI tools may allow pa-
tients to ask follow-up questions in a more familiar, conversational
format. It is possible that the back-and-forth supported by modern
generative AI tools will empower patients with greater information
about AI-driven decision-making, at which point patients may be
better-equipped to decide whether to trust (or not trust)
AI-generated medical recommendations.
Other challenges to AI adoption include pushback from health-
care practitioners—who wish to ensure high quality, experience-
driven patient care, or who fear being replaced by machines—
and from those managing healthcare systems, who might be re-
luctant to initiate costly and systemic changes until the useful-
ness of AI-integration is fully proven.
Insurance markets will also be impacted. Insurers could use AI
to rene their practices, capturing a larger share of the surplus.
This could lead to welfare losses for consumers. Today, it is not
possible to determine highly accurate, individualized probabilities
for the future health conditions of a particular insurant—insur-
ance as a eld relies instead on population-level probabilities,
with some renement from explicit risk factors. However, if gen-
erative AI allows companies to more accurately estimate this
probability—for example, by incorporating information from un-
observable factors that are identiable only through advanced
machine learning algorithms run on text-based claims, electronic
health records, or other data—they might charge higher premi-
ums to those at greater risk without offering reductions to those
at lower risk. This could remove health care access to high-risk
populations in private insurance-based systems. Use of AI is
prime for abuse by insurers who wish to maximize prots over en-
suring equitable access to healthcare.
AI could also enable insurers to reach currently uninsured
groups, reducing inefciencies and achieving market complete-
ness. A concrete example of this is the use of Responsible
Articial Intelligence in healthcare to predict and prevent insur-
ance claim denials, which could lead to signicant cost savings
and improved patient well-being (123). Moreover, the application
of AI by insurance companies might allow for a more accurate
prediction of loss probabilities, thus reducing one of the industry’s
most inherent problems, namely asymmetric information (124).
Generative AI may come to fulll social needs for some people,
which could have downstream effects on health. There is robust
evidence linking social connectedness or lack thereof to long-term
health outcomes (125), including increased risk for chronic ill-
nesses such as cardiovascular disease and stroke (126), type 2 dia-
betes, and dementia (127), as well as mortality from all causes
(128). While digitally mediated forms of socializing (e.g. social
media) have been utilized for years, there is increasing concern
about the implications of these platforms for mental, social, and
physical health (129). Generative AI can be used as a conversation-
al companion, potentially replacing some human interactions.
Indeed, digital proxies for social connection may, with increasing
sophistication, mimic features of social connection, which could
in turn decrease motivation to develop authentic human relation-
ships. These features may relieve some of the tensions of human
connection, leading people to preferentially spend more time with
AI than humans or even form pseudo-social attachments to AI
systems.
If AI-based chatbots are insufcient stand-ins for customary hu-
man interactions (which is likely true), then many negative conse-
quences could result. Humans are social beings, so our biological
systems can become dysregulated when social needs are unmet,
leading to poorer health (130). Therefore, it is essential that some
key elements of customary human interactions be retained. For ex-
ample, research nds that relative to emails and other text-based in-
teractions, those involving human voice boost social connection
(131). At the same time, AI-based chatbots could be useful to add so-
cial experiences for some individuals (while not completely replacing
human-to-human interaction), particularly for those facing difcul-
ties developing relationships on their own (who need “Vitamin S,”
from Social contact; (132), but are likely to be a poor or even danger-
ous replacement for human interaction writ large.
In sum, generative AI presents signicant opportunities to alle-
viate inequalities in physical and mental health, in addition to
augmenting healthcare providers’ capabilities. However, it is cru-
cial to ensure that generative AI are only designed to supplement,
rather than replace, human social interactions. Excessive depend-
ence on AI for social engagement could lead to various adverse
outcomes, including social isolation and deteriorating mental
and physical health. Table 4outlines key areas for future re-
search, along with specic example questions, potential designs,
and theoretical trade-offs. See Table S4 for an extended list of re-
search questions.
Policymaking in the age of AI
Regulation of AI
The rapid popularization of generative AI models has prompted
many governments worldwide to begin building regulatory frame-
works. The challenges raised by generative AI are global in nature
(133). However, the responses to these challenges so far have been
specic to individual countries or areas. In this article, we focus on
the regulatory responses of the European Union, United States,
and United Kingdom. Regulations are also being developed in oth-
er major countries, including China and India (134, 135).
The European Union’s AI Act has emerged as one of the rst
major attempts to provide a legal framework for the development
and deployment of AI. The act aims to address the challenges
posed by AI technologies while fostering innovation and trust in
AI applications (136). This initiative comes with several pros.
Firstly, it introduces a risk-based regulatory approach, distin-
guishing among banned, high-risk, and low-risk AI applications.
This categorization ensures that AI systems with signicant impli-
cations for individuals’ rights and safety are subject to stricter
scrutiny and compliance requirements. Secondly, the Act empha-
sizes transparency and accountability in AI systems, requiring
clear information about how AI decisions are made, particularly
in high-risk scenarios. Additionally, the Act promotes ethical AI
development, focusing on fundamental rights, nondiscrimina-
tion, and privacy. However, the Act is not without its cons (137).
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The broad denitions and categories within the Act pose chal-
lenges, creating potential uncertainty for AI developers and users.
Further, the strict regulations might place European Union com-
panies at a competitive disadvantage globally, particularly
against rms in regions with more lenient AI laws.
In contrast, the United States has historically had a more frag-
mented approach, with various federal and state-level initiatives
rather than a single, comprehensive legislative framework (138).
This approach has its advantages. For one, it allows for more
exibility and adaptability in regulation, catering to the diverse
range of AI applications and industries in the United States. It
also promotes a more innovation-friendly environment by
avoiding overly prescriptive rules that could hinder technological
advancement. However, it also has notable disadvantages. The
lack of a unied regulatory framework can lead to inconsistencies
and uncertainties, potentially creating a complex patchwork of
regulations for AI companies to navigate. This fragmented ap-
proach might also lag in addressing broader ethical and social
concerns about AI, such as privacy, bias, and accountability.
Further, without a cohesive national strategy, the United States
risks falling behind in setting global standards for AI governance.
On 2023 October 30, President Biden issued an Executive Order on
Safe, Secure, and Trustworthy Articial Intelligence, which di-
rects the development of new guidelines, reports, and governance
structures relating to AI, representing an effort to establish a more
cohesive federal policy on AI (139).
In the United Kingdom, the government has published a White
Paper advocating for a proinnovation approach, particularly in
commercial applications of AI (140). While the White Paper recog-
nizes the risks of AI and the challenge of building public trust, it
Table 4. Summary of the main research directions on the impact of generative AI on healthcare, along with specic example questions.
Future research directions
Research area Specic question Potential design Trade-off
Research how AI can assist
healthcare professionals in
diagnosis, treatment planning,
and patient monitoring.
Does AI-assisted medical decision
making improve patient
outcomes including decision
accuracy and bias reduction?
Healthcare providers are given the
option to consult or not consult
medical advice from generative AI
chatbots, with decision accuracy
and decision bias measured across
medical contexts and healthcare
provider characteristics.
Clinicians could heed the advice of
AI, improving their medical
decision making. However,
mistrust for AI tools, low AI
literacy, or other sources of
suboptimal interactions with AI
could render AI-assisted decision
making ineffective or detrimental
for patient outcomes.
Investigate the use of AI to reduce
the administrative burden on
healthcare providers through
efcient electronic health
records (EHR) management.
Does the integration of AI into EHR
systems reduce the time
healthcare providers spend on
documentation compared to
traditional EHR systems?
Healthcare providers are divided into
two groups, one using
AI-integrated EHR systems and the
other using traditional EHR
systems, with the time spent on
documentation measured over a
specied period.
AI could streamline documentation
processes, reducing time spent on
paperwork. However, learning
curves associated with new AI
systems might initially increase the
time required for documentation.
Study how AI can contribute to the
development of personalized
medicine, adapting treatments
to individual patient needs and
reducing healthcare disparities.
Can AI-based predictive models
accurately identify patients at
high-risk for diabetes (or any
other disease) and guide early
intervention strategies?
Develop AI models using patient
health data to predict diabetes risk
and apply intervention strategies
based on model predictions.
Compare the incidence of diabetes
in this group with a control group
receiving standard care.
Predictive models could allow for
earlier and more targeted
interventions. However,
inaccuracies in predictions could
lead to unnecessary interventions
or miss at-risk individuals.
Investigate strategies to increase
public trust and understanding
of AI in healthcare.
Does providing patients with
detailed explanations of AI
diagnostic processes improve
their trust in AI-driven
healthcare?
Patients receiving AI-driven
diagnostics are split into two
groups, one receiving detailed
explanations and the other
receiving standard information.
Trust levels are measured
postinteraction.
Detailed explanations could
demystify AI processes, increasing
trust. However, overly technical or
complex information might
overwhelm patients, potentially
reducing trust.
Research how AI can improve
healthcare accessibility in
underserved regions and
populations, in both rural and
urban areas.
Can AI-driven mobile health
applications effectively increase
healthcare access in rural
communities?
Implement an AI-driven mobile
health application in rural
communities. Compare health
outcomes, access to care, and
self-management behaviors with a
control group not using the
application.
Mobile health apps could
signicantly improve access and
self-management, but poor
internet connectivity and low
digital literacy in rural areas might
limit effectiveness and even widen
disparities.
Investigate the potential of AI to
facilitate social connections,
particularly for individuals with
difculties in forming
relationships, while also
studying the potential risks of
over-reliance on AI for social
interaction.
Can AI-based chatbots effectively
reduce feelings of loneliness
compared to traditional social
programs?
Participants are divided into two
groups, with one interacting with
AI-based chatbots and the other
engaging in traditional social
programs. Measures of loneliness
and social skills are assessed over
time and their inuence on health.
AI agents could offer constant
companionship, potentially
reducing loneliness. However,
over-reliance on AI could inhibit
motivation for human interaction
leading to increased isolation.
Further, the lack of genuine human
interaction might not fulll deep
social needs, affecting overall
health.
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refrains from proposing a regulatory framework to encourage
innovation. Instead, the White Paper outlines some “cross-
sectoral” nonstatutory soft principles: safety, security, robust-
ness, appropriate transparency and explainability, fairness,
accountability and governance, and contestability and redress.
The White Paper opts against a specialist AI regulator, preferring
to support existing regulators in integrating AI considerations.
Furthermore, the focus on commercial innovations has drawn
criticism for overlooking the increasing use of AI in government
sectors like healthcare and education. One leading nongovern-
mental organization, the Public Law Project, led a civil society
coalition to produce Key principles for an alternative AI white paper
(141), which argues that an alternative vision is necessary.
Amongst other proposals, the alternative white paper argues
that: government use of AI must be transparent, transparency re-
quirements must be mandatory, there must be clear mechanisms
for accountability, the public should be consulted about new au-
tomated decision-making tools before they are deployed by gov-
ernment, there must be a specialist regulator to enforce the
regulatory regime and ensure people can seek redress when
things go wrong, and uses of AI that threaten fundamental rights
should be prohibited.
We believe that the regulations of the European Union, the
United States, and the United Kingdom do not pay sufcient at-
tention to socioeconomic inequalities. In the following, we outline
several key interventions currently missing from these regulatory
frameworks (80). See Table 5for a summary.
Tax system: It has long been recognized that tax codes in many
developed countries often place a heavier burden on rms that
hire labor than on those that invest in algorithms to automate
work (142, 143). This has resulted in a lower share of income to labor
while capital investments are rewarded. We should aim to create a
more symmetric tax structure, where marginal taxes for hiring
(and training) labor and for investing in equipment/software are
equated. This would help to shift incentives toward human-
complementary technological choices by reducing the bias of the
tax code toward physical capital over human capital. As Bill Gates
declared back in 2017, “If a robot comes in to do the same thing,
you’d think that we’d tax the robot at a similar level” (144).
Labor voice and control of consumer information: Given that
AI will have tremendous impact across industries and throughout
society, it would be prudent to ensure that workers and civil soci-
ety have a voice in this change. Health and safety rules should also
be updated accordingly. In addition, data unions could be helpful
to put the power and benets of user data back in the hands of
consumers. Given the concerns that a handful of very large com-
panies will control the direction of generative AI, it is reasonable
that users be compensated for the use of their information, or en-
abled to support other emergent competitors to predominant
market players like Microsoft and Google.
Funding for more human-complementary research: Because
the current path of research is biased toward automation (11,
68), support for research and development of human-
complementary AI technologies could offer strong upsides for
growth. It is most feasible to focus on specic sectors and activ-
ities where opportunities are already abundant. These include
education, healthcare, and modern craft worker training—where
the information provisional capabilities of AI systems could boost
productivity and enable workers to earn higher wages by aug-
menting their skills. In the United States, DARPA orchestrated in-
vestments and competitions to foster development of self-driving
cars and dexterous robotics—in a similar fashion, governments
should encourage competition and investment that pairs AI tools
with human expertise, aiming to improve work in vital social
sectors.
Professional development and training: Investment in profes-
sional development and training is crucial for professionals such
as educators and healthcare workers to effectively integrate AI
tools into their work. Training programs should focus on the cap-
abilities and limitations of AI, include ethical considerations, and
teach technical skills required to interact with AI systems. Such
training will empower professionals to use AI as a complementary
tool that enhances their skills.
Combating AI-generated misinformation: Given the substan-
tial impact that generative AI can have on misinformation, it is
critical for governments to invest in combating AI-generated
misinformation. Tools and standards to identify AI-generated
content, including text, images, audio, and video, should be devel-
oped. Additionally, educational campaigns should be initiated, to
reduce general susceptibility to misinformation and provide the
public with improved fact-checking strategies. A task force com-
posed of policymakers, technology companies, and social scien-
tists could help develop practical methods to effectively combat
a potential infodemic.
Governmental and consultative expertise: To foster human-
complementary AI integration, it is fundamental to have AI
expertise within the government. AI will touch every area of gov-
ernment investment, regulation, and oversight. Developing con-
sultative AI bodies that can advise governments and support the
many agencies and regulators tackling these challenges will sup-
port more timely and effective decision-making. Initiatives like
the High-Level Expert Group on Articial Intelligence, established
by the European Union, the Responsible Technology Adoption
Unit, set by the United Kingdom, and the Articial Intelligence
topic in the United States National Institute of Standards and
Technology represent signicant progresses in this direction.
Regulation using AI
Generative AI holds enormous potential to provide policy sugges-
tions, due to its capacity to analyze vast amounts of data, recog-
nize complex patterns, and offer insights that might elude
human analysis. Such analysis can uncover hidden relationships
and forecast future trends, providing a data-driven foundation for
Table 5. Policy recommendations for mitigating socioeconomic
inequalities potentially caused by generative AI in the workplace,
education, healthcare, and information, and not covered by
current regulatory approach in the European Union, United
States, and United Kingdom.
Policy recommendations
Create a more symmetric tax structure, where marginal taxes for hiring
and training labor and for investing in the development, installation,
and usage of new AI tools are equated.
Involve workers and civil society in AI-related changes and establish data
unions to empower data owners with control over their data.
Increase support for research into human-complementary AI tools to
enhance productivity and workers’ skillsets.
Train professionals, especially in education and healthcare, in the use of
AI tools, covering their capabilities, limitations, and ethical
considerations.
Invest in strategies aimed to combat AI-generated misinformation,
including developing tools that can identify AI-generated
misinformation and initiating educational campaigns.
Embed AI expertise within government to advise and support
decision-making across various sectors.
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policy decisions. Moreover, AI’s potential to simulate various pol-
icy outcomes based on historical data and predictive models can
aid policymakers in understanding the potential impacts of their
decisions (145, 146) However, the ethical and practical concerns
of using AI for policymaking are signicant and perhaps even pro-
hibitive with the current tools available.
For AI-powered systems to be reliable decision-making assis-
tants, they must be able to understand and complement human
behavior in decision-making contexts. An emerging line of re-
search has begun investigating how chatbots powered by large
language models make decisions when asked to behave like hu-
mans in various contexts, including cooperative, altruistic, trust,
moral, risk, time, and food scenarios (147, 148) In synthesis, these
ndings suggest that chatbots behavior is similar to human be-
havior, leading to the suggestion that “GPT could have the poten-
tial in assisting human decision-making” (147). Furthermore,
sentiment scores provided by GPT-4 have been shown to explain
how humans balance self-interest and the interest of others, be-
yond economic models based solely on monetary outcomes
(149). While this research highlights the ability of large language
models to simulate human behavior, it has also been observed
that GPT-4 consistently underestimates people’s self-interest
and inequity-aversion, while overestimating their level of altruis-
tic behavior (150). This “optimistic bias” carries important conse-
quences for those creating and using AI, as assumptions of
excessive human prosociality could result in disillusionment,
frustration, and even social tensions (150).
Additionally, the alignment between AI-powered decision-
making assistants and humans will inevitably become more com-
plex in situations with multiple and potentially competing values.
Humans have a wide range of culturally diverse beliefs about right
and wrong, and aligning AI systems to human values and prefer-
ences is challenging even within narrow domains such as auto-
mated driving (151). Aligning generative AI, especially in the
domain of policy recommendations, becomes even more challen-
ging. For example, consider different normative principles that
have been identied for trustworthy ethical AI (152). It is argued
that AI should, amongst other things, promote benecence (pro-
mote human well-being and welfare); nonmalecence (not cause
harm and generate outputs that assist in carrying out illegal,
harmful, or immoral actions); justice (preserve fairness, justice,
and solidarity: it should not generate outputs that discriminate
against certain groups, especially marginalized groups), and en-
sure autonomy (respecting human freedom and ensuring humans
should choose how and whether to delegate policy decisions).
While these principles are all defensible in the abstract—forming
the basis of much normative ethical theory and applied ethics—
challenges will inevitably arise when these principles conict.
Generative AI may assist in generating policies that maximize
overall aggregate welfare, in line with utilitarian philosophy
(153) but in doing so infringe on human rights (neglecting the prin-
ciple of autonomy) or recommending some harm to a smaller
group for the benet of the majority (neglecting the principle of
nonmalecence). There is no consensus amongst laypeople about
how such moral dilemmas should be resolved (154, 155), nor is
there normative agreement amongst philosophers on how they
should be resolved and why. This discord among human thinkers
underscores the challenge of programming AI to make policy de-
cisions that involve moral trade-offs.
To make an explicit example, consider the following three
high-level, high-priority constraints for aligned chatbots: (i) to
not cause harm or provide dangerous information; (ii) to not gen-
erate outputs that discriminate against certain groups, and (iii) to
be culturally sensitive. While these objectives are all desirable,
they are increasingly difcult to reconcile. If the only constraint
is to avoid dangerous information (nonmalecence), regardless
of social neutrality or cultural sensitivity (justice), one can use re-
inforcement learning from human feedback using a convenience
sample of annotators. But, this approach would fail to ensure so-
cial neutrality, since a convenience sample of annotators would
have unrepresentative biased views on what constitutes immoral
actions or generally undesirable outputs (156). Political and social
neutrality may be approximated by engaging in carefully bal-
anced reinforcement learning from human feedback, based on a
broadly representative array of opinion, or by having a singular
chatbot that facilitates consensus-making among diverse human
values (157). Alternatively, an ecosystem of chatbots with diverse
systems of values—liberal and conservative bots, secular and re-
ligious bots, etc.—may emerge. These chatbots can each focus
on their specic domain, while also undergoing a political process
to achieve collective decisions among themselves. However, these
approaches would still fail at cultural sensitivity, since different
cultures may be different in terms of the social groups they in-
clude, the topics these groups value, and the range of these cul-
tural values (158).
In the worst case scenario, then, the alignment of generative AI
would be entirely based on the views of a small group of socially,
politically, and culturally homogeneous informants (11). But even
in the best-case scenario, where generative AI is trained on a di-
verse and nuanced set of preferences, we would still have signi-
cant problems. Even if we have a more diverse set of information
about humans’ actual values and how they might want trade-offs
to be made for moral dilemmas, we still lack widespread agree-
ment on a specic normative standard to justify these descriptive
preferences.
Aside from the alignment problem, there is the implementation
problem: how to equip policymakers with reliable support from or-
ganizations and specialized staff. Most policymakers currently lack
the knowledge and skills to directly evaluate the extent to which
AI-based generative chatbots may embed undesired preferences or
detrimental systematic biases. Admittedly, one can hardly expect
that policymakers can acquire the needed knowledge and skills in
due time. So, policymakers are likely to become the “principals” in
a principal-agent problem, struggling to consider the preferences
of their “AI-agents”. Policymakers will have to rely on some other
agent for this evaluation, based on scientic principles for character-
izing machine behavior and misbehavior (159, 160).
Hence, it is crucial to design supporting organizations that sys-
tematically provide the policymakers with: (i) frequent evaluation
of the current state of alignment between legal and regulatory re-
quirements and (ii) mechanisms to signal any legal and regulatory
changes in those requirements to companies that use AI-based
chatbots—thus putting society in the loop (159). These desiderata
in turn require the construction of a dedicated ofce in the organ-
ization that monitors AI-based chatbots, considering—and pos-
sibly predicting their evolution.
Finally, there is also a philosophical problem. Even if we
could solve the problems of conicting preferences, even if we could
generate a good culturally sensitive sample, and even if we could
solve the implementation problem—should we?
Conclusion
The future will likely be starkly different from anything we have
experienced before. But the effects of generative AI will ultimately
depend on the choices that we make to design and deploy the
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technology. We stand at a unique and historical moment; our de-
cisions and actions today will shape the trajectory of our future.
This responsibility extends to all sectors of society, including gov-
ernance, scientic research, industry, and the public.
We have focused on the socioeconomic inequalities that are
likely to be impacted—for better or worse—by the advent of gen-
erative AI. This technology has profound implications in the do-
main of information, where it has the potential to offer more
tailored, efcient, and democratic ways to process information.
Yet it also poses several challenges, including anticompetitive
market advantages, data misuse and abuse, and misinformation.
These mixed outcomes will certainly affect a very wide range of
social organization and decision making.
Here, we focused specically on the domains of work, educa-
tion, and healthcare. For instance, in the workplace, AI could
automate some job tasks, create new work, but also change
wage distributions, and require new skill sets. In education, AI
could democratize learning and provide personalized education
solutions, but also increase the digital divide. In the healthcare
sector, AI’s ability to analyze large datasets can lead to better pa-
tient outcomes, but it also raises questions about equitable access
to AI-driven healthcare services and genuine human interactions.
We have outlined several research questions that urgently
require answers to address these issues effectively. These ques-
tions aim to harness AI’s benets while mitigating its risks.
Additionally, we have observed that current regulatory approaches
in the European Union, United States, and United Kingdom some-
times fail to adequately address these challenges. There is a need
for a dynamic regulatory framework that can keep pace with the
rapid advancements in AI technology.
Our hope is that this work contributes to a comprehensive re-
search agenda and public debates on these critical topics. As we
noted at the beginning of this article, the rise of powerful AI will
be either the best, or the worst thing, ever to happen to humanity.
We may not yet know which, because we do not know how hu-
mans will react to this technology. As we stand at the cusp of
this new era of human–machine interactions, it is crucial that
we engage in thoughtful and inclusive discussions about the
role of AI in shaping our society, because the decisions we make
today will have lasting impacts on generations to come.
Supplementary Material
Supplementary material is available at PNAS Nexus online.
Funding
D.A. acknowledges support from the Hewlett Foundation, the
US National Science Foundation, Schmidt Sciences, Google,
the Sloan Foundation, the Smith Richardson Foundation, and the
Washington Center for Equitable Growth. J.F.B. acknowledges sup-
port from grant ANR-19-PI3A-0004, grant ANR-17-EURE-0010, and
the research foundation TSE-Partnership. P.B.G. acknowledges sup-
port from the Spanish Ministry of Science and Innovation
(PID2021-126892NB-I00). K.D. acknowledges support from the ERC
Advanced Grant “Consequences of conspiracy theories—
CONSPIRACY_FX” Number: 101018262. J.A.C.E. acknowledges sup-
port by the ESRC (ES/V015176/1) and Leverhulme Trust
(PLP-2021-095). J.J. acknowledges support from the Australian
Research Council Laureate Fellowship (FL180100094). S.J. acknowl-
edges support from the Hewlett Foundation and the MIT Sloan
School, MIT. S.N. acknowledges support from the University of
Turin, Italy, Grant “Human-Machine Communication Cultures:
Articial Intelligence, Media and Cultures in a Global Context”, refer-
ence number NATS_GFI_22_01_F. P.V.L. acknowledges support from
an Ammodo science award (2020) provided by the Royal Netherlands
Academy of Arts and Sciences. J.V.B. acknowledges support from the
Trust in generative AI, Google Jigsaw, and from the Center for
Conict and Cooperation, Templeton World Charity Foundation,
Templeton World Charity Foundation (TWCF-2022-30561). This
manuscript was posted on a preprint server: https://papers.ssrn.
com/sol3/papers.cfm?abstract_id=4666103
Author Contributions
V.C. conceptualization; supervision; investigation; methodology;
writing-original draft; project administration; writing-review
and editing; A.L., D.A., S.A., A.A., E.B., J.-F.B., P.B.-G., L.B., K.D.,
J.E., G.G., C.G., D.H., J.H.-L., J.J., and S.J. conceptualization; investi-
gation; writing-original draft; writing-review and editing; W.K.
conceptualization; investigation; writing-review and editing.
C.L., P.L., and S.N. conceptualization; investigation; writing-
original draft; writing-review and editing; S.P. conceptualization;
investigation; writing-review and editing; I.R., N.S., V.S., S.S., J.S.,
J.T., S.v.d.L., P.V.L., and F.W. conceptualization; investigation;
writing-original draft; writing-review and editing; J.V.B. con-
ceptualization; supervision; investigation; writing-original draft;
writing-review and editing; R.V. conceptualization; supervision;
investigation; methodology; writing-original draft; writing-review
and editing.
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