How AI Threatens Democracy
Sarah Kreps, Doug Kriner
Journal of Democracy, Volume 34, Number 4, October 2023, pp. 122-131
(Article)
Published by Johns Hopkins University Press
DOI:
For additional information about this article
https://doi.org/10.1353/jod.2023.a907693
https://muse.jhu.edu/article/907693
[89.176.127.177] Project MUSE (2024-09-24 19:13 GMT)
Journal of Democracy Volume 34, Number 4 October 2023
© 2023 National Endowment for Democracy and Johns Hopkins University Press
HOW AI THREATENS
DEMOCRACY
Sarah Kreps and Doug Kriner
Sarah Kreps is the John L. Wetherill Professor in the Department of
Government, adjunct professor of law, and the director of the Tech Policy
Institute at Cornell University. Doug Kriner is the Clinton Rossiter
Professor in American Institutions in the Department of Government at
Cornell University. 
Just a month after its introduction, ChatGPT, the generative artificial
intelligence (AI) chatbot, hit 100-million monthly users, making it the
fastest-growing application in history. For context, it took the videostreaming
service Netflix, now a household name, three-and-a-half
years to reach one-million monthly users. But unlike Netflix, the meteoric
rise of ChatGPT and its potential for good or ill sparked considerable
debate. Would students be able to use, or rather misuse, the tool for
research or writing? Would it put journalists and coders out of business?
Would it “hijack democracy,” as one New York Times op-ed put it, by
enabling mass, phony inputs to perhaps influence democratic represen-
tation?1
And most fundamentally (and apocalyptically), could advances
in artificial intelligence actually pose an existential threat to humanity?2
New technologies raise new questions and concerns of different magnitudes
and urgency. For example, the fear that generative AI—artificial
intelligence capable of producing new content—poses an existential
threat is neither plausibly imminent, nor necessarily plausible. Nick
Bostrom’s paperclip scenario, in which a machine programmed to optimize
paperclips eliminates everything standing in its way of achieving
that goal, is not on the verge of becoming reality.3
Whether children or
university students use AI tools as shortcuts is a valuable pedagogical
debate, but one that should resolve itself as the applications become
more seamlessly integrated into search engines. The employment consequences
of generative AI will ultimately be difficult to adjudicate
since economies are complex, making it difficult to isolate the net effect
of AI-instigated job losses versus industry gains. Yet the potential
Artificial Intelligence and Democracy
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123Sarah Kreps and Doug Kriner
consequences for democracy are immediate and severe. Generative AI
threatens three central pillars of democratic governance: representation,
accountability, and, ultimately, the most important currency in a political
system—trust.
The most problematic aspect of generative AI is that it hides in plain
sight, producing enormous volumes of content that can flood the media
landscape, the internet, and political communication with meaningless
drivel at best and misinformation at worst. For government officials, this
undermines efforts to understand constituent sentiment, threatening the
quality of democratic representation. For voters, it threatens efforts to
monitor what elected officials do and the results of their actions, eroding
democratic accountability. A reasonable cognitive prophylactic measure
in such a media environment would be to believe nothing, a nihilism
that is at odds with vibrant democracy and corrosive to social trust. As
objective reality recedes even further from the media discourse, those
voters who do not tune out altogether will likely begin to rely even more
heavily on other heuristics, such as partisanship, which will only further
exacerbate polarization and stress on democratic institutions.
Threats to Democratic Representation
Democracy, as Robert Dahl wrote in 1972, requires “the continued
responsiveness of the government to the preferences of its citizens.”4
For elected officials to be responsive to the preferences of their constituents,
however, they must first be able to discern those preferences.
Public-opinion polls—which (at least for now) are mostly immune from
manipulation by AI-generated content—afford elected officials one
window into their constituents’ preferences. But most citizens lack even
basic political knowledge, and levels of policy-specific knowledge are
likely lower still.5
As such, legislators have strong incentives to be the
most responsive to constituents with strongly held views on a specific
policy issue and those for whom the issue is highly salient. Written correspondence
has long been central to how elected officials keep their
finger on the pulse of their districts, particularly to gauge the preferences
of those most intensely mobilized on a given issue.6
In an era of generative AI, however, the signals sent by the balance
of electronic communications about pressing policy issues may be severely
misleading. Technological advances now allow malicious actors
to generate false “constituent sentiment” at scale by effortlessly creating
unique messages taking positions on any side of a myriad of issues.
Even with old technology, legislators struggled to discern between
human-written and machine-generated communications.
In a field experiment conducted in 2020 in the United States, we composed
advocacy letters on six different issues and then used those letters
to train what was then the state-of-the-art generative AI model, GPT-3,
124 Journal of Democracy
to write hundreds of left-wing and right-wing advocacy letters. We sent
randomized AI- and human-written letters to 7,200 state legislators, a
total of about 35,000 emails. We then compared response rates to the
human-written and AI-generated correspondence to assess the extent to
which legislators were able to discern (and therefore not respond to)
machine-written appeals. On three issues, the response rates to AI- and
human-written messages were statistically indistinguishable. On three
other issues, the response rates to AI-generated emails were lower—but
only by 2 percent, on average.7
This suggests that a malicious actor capable
of easily generating thousands of unique communications could
potentially skew legislators’ perceptions of which issues are most important
to their constituents as well as how constituents feel about any
given issue.
In the same way, generative AI could strike a double blow against the
quality of democratic representation by rendering obsolete the publiccomment
process through which citizens can seek to influence the actions
of the regulatory state. Legislators necessarily write statutes in
broad brushstrokes, granting administrative agencies considerable discretion
not only to resolve technical questions requiring substantive expertise
(e.g., specifying permissible levels of pollutants in the air and
water), but also to make broader judgements about values (e.g., the
acceptable tradeoffs between protecting public health and not unduly
restricting economic growth).8
Moreover, in an era of intense partisan
polarization and frequent legislative gridlock on pressing policy priorities,
U.S. presidents have increasingly sought to advance their policy
agendas through administrative rulemaking.
Moving the locus of policymaking authority from elected representatives
to unelected bureaucrats raises concerns of a democratic deficit.
The U.S. Supreme Court raised such concerns in West Virginia v. EPA
(2022), articulating and codifying the major questions doctrine, which
holds that agencies do not have authority to effect major changes in
policy absent clear statutory authorization from Congress. The Court
may go even further in the pending Loper Bright Enterprises v. Raimondo
case and overturn the Chevron doctrine, which has given agencies
broad latitude to interpret ambiguous congressional statutes for nearly
three decades, thus further tightening the constraints on policy change
through the regulatory process.
Not everyone agrees that the regulatory process is undemocratic,
however. Some scholars argue that the guaranteed opportunities for public
participation and transparency during the public-notice and comment
period are “refreshingly democratic,”9
and extol the process as “democratically
accountable, especially in the sense that decision-making is
kept above board and equal access is provided to all.”10
Moreover, the
advent of the U.S. government’s electronic-rulemaking (e-rulemaking)
program in 2002 promised to “enhance public participation . . . so as
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125Sarah Kreps and Doug Kriner
to foster better regulatory decisions” by lowering the barrier to citizen
input.11
Of course, public comments have always skewed, often heavily,
toward interests with the most at stake in the outcome of a proposed
rule, and despite lowering the barriers to engagement, e-rulemaking did
not alter this fundamental reality.12
Despite its flaws, the direct and open engagement of the public in
the rulemaking process helped to bolster the democratic legitimacy of
policy change through bureaucratic action. But the ability of malicious
actors to use generative AI to flood e-rulemaking platforms with limitless
unique comments advancing a particular agenda could make it all
but impossible for agencies to learn about genuine public preferences.
An early (and unsuccessful) test case arose in 2017, when bots flooded
the Federal Communications Commission with more than eight-million
comments advocating repeal of net neutrality during the open comment
period on proposed changes to the rules.13
This “astroturfing” was detected,
however, because more than 90 percent of those comments were
not unique, indicating a coordinated effort to mislead rather than genuine
grassroots support for repeal. Contemporary advances in AI technology
can easily overcome this limitation, rendering it exceedingly difficult
for agencies to detect which comments genuinely represent the
preferences of interested stakeholders.
Threats to Democratic Accountability
A healthy democracy also requires that citizens be able to hold government
officials accountable for their actions—most notably, through
free and fair elections. For ballot-box accountability to be effective,
however, voters must have access to information about the actions taken
in their name by their representatives.14
Concerns that partisan bias in
the mass media, upon which voters have long relied for political information,
could affect election outcomes are longstanding, but generative
AI poses a far greater threat to electoral integrity.
As is widely known, foreign actors exploited a range of new technologies
in a coordinated effort to influence the 2016 U.S. presidential
election. A 2018 Senate Intelligence Committee report stated:
Masquerading as Americans, these (Russian) operatives used targeted advertisements,
intentionally falsified news articles, self-generated content, and
social media platform tools to interact with and attempt to deceive tens of
millions of social media users in the United States. This campaign sought to
polarize Americans on the basis of societal, ideological, and racial differences,
provoked real world events, and was part of a foreign government’s covert
support of Russia’s favored candidate in the U.S. presidential election.15
While unprecedented in scope and scale, several flaws in the influence
campaign may have limited its impact.16
The Russian operatives’ social-
126 Journal of Democracy
media posts had subtle but noticeable grammatical errors that a native
speaker would not make, such as a misplaced or missing article—telltale
signs that the posts were fake. ChatGPT, however, makes every user the
equivalent of a native speaker. This technology is already being used to
create entire spam sites and to flood sites with fake reviews. The tech
website The Verge flagged a job seeking an “AI editor” who could generate
“200 to 250 articles per week,” clearly implying that the work would
be done via generative AI tools that can churn out mass quantities of
content in fluent English at the click of the editor’s “regenerate” button.17
The potential political applications are myriad. Recent research shows
that AI-generated propaganda is just as believable as propaganda written
by humans.18
This, combined with new capacities for microtargeting,
could revolutionize disinformation campaigns, rendering them far more
effective than the efforts to influence the 2016 election.19
A steady stream
of targeted misinformation could skew how voters perceive the actions
and performance of elected officials to such a degree that elections cease
to provide a genuine mechanism of accountability since the premise of
what people are voting on is itself factually dubious.20
Threats to Democratic Trust
Advances in generative AI could allow malicious actors to produce
misinformation, including content microtargeted to appeal to specific
demographics and even individuals, at scale. The proliferation of socialmedia
platforms allows the effortless dissemination of misinformation,
including its efficient channeling to specific constituencies. Research
suggests that although readers across the political spectrum cannot
distinguish between a range of human-made and AI-generated content
(finding it all plausible), misinformation will not necessarily change
readers’ minds.21
Political persuasion is difficult, especially in a polarized
political landscape.22
Individual views tend to be fairly entrenched,
and there is little that can change people’s prior sentiments.
The risk is that as inauthentic content—text, images, and video—
proliferates online, people simply might not know what to believe
and will therefore distrust the entire information ecosystem. Trust in
media is already low, and the proliferation of tools that can generate
inauthentic content will erode that trust even more. This, in turn,
could further undermine perilously low levels of trust in government.
Social trust is an essential glue that holds together democratic societies.
It fuels civic engagement and political participation, bolsters
confidence in political institutions, and promotes respect for democratic
values, an important bulwark against democratic backsliding
and authoritarianism.23
Trust operates in multiple directions. For political elites, responsiveness
requires a trust that the messages they receive legitimately
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127Sarah Kreps and Doug Kriner
represent constituent preferences and not a coordinated campaign to
misrepresent public sentiment for the sake of advancing a particular
viewpoint. Cases of “astroturfing” are nothing new in politics, with
examples in the United States dating back at least to the 1950s.24
However,
advances in AI threaten to make such efforts ubiquitous and
more difficult to detect.
For citizens, trust can motivate political participation and engagement,
and encourage resistance against threats to democratic institutions
and practices. The dramatic decline in Americans’ trust in government
over the past half century is among the most documented developments
in U.S. politics.25
While many factors have contributed to this erosion,
trust in the media and trust in government are intimately linked.26
Bombarding
citizens with AI-generated content of dubious veracity could
seriously threaten confidence in the media, with severe consequences
for trust in the government.
Mitigating the Threats
Although understanding the motives and technology is an important
first step in framing the problem, the obvious next step is to formulate
prophylactic measures. One such measure is to train and deploy the
same machine-learning models that generate AI to detect AI-generated
content. The neural networks used in artificial intelligence to create text
also “know” the types of language, words, and sentence structures that
produce that content and can therefore be used to discern patterns and
hallmarks of AI-generated versus human-written text. AI detection tools
are proliferating quickly and will need to adapt as the technology adapts,
but a “Turnitin”-style model—like those that teachers use to detect plagiarism
in the classroom—may provide a partial solution. These tools
essentially use algorithms to identify patterns within the text that are
hallmarks of AI-generated text, although the tools will still vary in their
accuracy and reliability.
Even more fundamentally, the platforms responsible for generating
these language models are increasingly aware of what it took many
years for social-media platforms to realize—that they have a responsibility
in terms of what content they produce, how that content is framed,
and even what type of content is proscribed. If you query ChatGPT
about how generative AI could be misused against nuclear command
and control, the model responds with “I’m sorry, I cannot assist with
that.” OpenAI, the creator of ChatGPT, is also working with external
researchers to democratize the values encoded in their algorithms, including
which topics should be off limits for search outputs and how to
frame the political positions of elected officials. Indeed, as generative
AI becomes more ubiquitous, these platforms have a responsibility not
just to create the technology but to do so with a set of values that is
128 Journal of Democracy
ethically and politically informed. The question of who gets to decide
what is ethical, especially in polarized, heavily partisan societies, is not
new. Social-media platforms have been at the center of these debates for
years, and now the generative AI
platforms are in an analogous situation.
At the least, elected public
officials should continue to work
closely with these private firms to
generate accountable, transparent
algorithms. The decision by seven
major generative AI firms to commit
to voluntary AI safeguards, in
coordination with the Biden Administration,
is a step in the right
direction.
Finally, digital-literacy campaigns
have a role to play in guarding
against the adverse effects of
generative AI by creating a more
informed consumer. Just as neural networks “learn” how generative
AI talks and writes, so too can individual readers themselves. After we
debriefed the state legislators in our study about its aims and design,
some said that they could identify AI-generated emails because they
know how their constituents write; they are familiar with the standard
vernacular of a constituent from West Virginia or New Hampshire. The
same type of discernment is possible for Americans reading content online.
Large language models such as ChatGPT have a certain formulaic
way of writing—perhaps having learned a little too well the art of the
five-paragraph essay.
When we asked the question, “Where does the United States have
missile silos?” ChatGPT replied with typical blandness: “The United
States has missile silos located in several states, primarily in the central
and northern parts of the country. The missile silos house intercontinental
ballistic missiles (ICBMs) as part of the U.S. nuclear deterrence
strategy. The specific locations and number of missile silos may vary
over time due to operational changes and modernization efforts.”
There is nothing wrong with this response, but it is also very predictable
to anyone who has used ChatGPT somewhat regularly. This example
is illustrative of the type of language that AI models often generate.
Studying their content output, regardless of the subject, can help people
to recognize clues indicating inauthentic content.
More generally, some of the digital-literacy techniques that have already
gained currency will likely apply in a world of proliferating AIgenerated
texts, videos, and images. It should be standard practice for
everyone to verify the authenticity or factual accuracy of digital content
The platforms responsible
for generating language
models are increasingly
aware of what it took many
years for social-media
platforms to realize—that
they have a responsibility in
terms of what content they
produce, how that content is
framed, and even what type
of content is proscribed.
[89.176.127.177]ProjectMUSE(2024-09-2419:13GMT)
129Sarah Kreps and Doug Kriner
across different media outlets and to cross-check anything that seems
dubious, such as the viral (albeit fake) image of the pope in a Balenciaga
puffy coat, to determine whether it is a deep fake or real. Such practices
should also help in discerning AI-generated material in a political context,
for example, on Facebook during an election cycle.
Unfortunately, the internet remains one big confirmation-bias machine.
Information that seems plausible because it comports with a person’s
political views may be less likely to drive that person to check the
veracity of the story. In a world of easily generated fake content, many
people may have to walk a fine line between political nihilism—that is,
not believing anything or anyone other than their fellow partisans—and
healthy skepticism. Giving up on objective fact, or at least the ability
to discern it from the news, would shred the trust on which democratic
society must rest. But we are no longer living in a world where “seeing
is believing.” Individuals should adopt a “trust but verify” approach to
media consumption, reading and watching but exercising discipline in
terms of establishing the material’s credibility.
New technologies such as generative AI are poised to provide enormous
benefits to society—economically, medically, and possibly even
politically. Indeed, legislators could use AI tools to help identify inauthentic
content and also to classify the nature of their constituents’
concerns, both of which would help lawmakers to reflect the will of the
people in their policies. But artificial intelligence also poses political
perils. With proper awareness of the potential risks and the guardrails
to mitigate against their adverse effects, however, we can preserve and
perhaps even strengthen democratic societies.
NOTES
1. Nathan E. Sanders and Bruce Schneier, “How ChatGPT Hijacks Democracy,” New
York Times, 15 January 2023, www.nytimes.com/2023/01/15/opinion/ai-chatgpt-lobbying-
democracy.html.
2. Kevin Roose, “A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn,” New York
Times, 30 May 2023, www.nytimes.com/2023/05/30/technology/ai-threat-warning.html.
3. Alexey Turchin and David Denkenberger, “Classification of Global Catastrophic
Risks Connected with Artificial Intelligence,” AI & Society 35 (March 2020): 147–63.
4. Robert Dahl, Polyarchy: Participation and Opposition (New Haven: Yale University
Press, 1972), 1.
5. Michael X. Delli Carpini and Scott Keeter, What Americans Know about Politics
and Why it Matters (New Haven: Yale University Press, 1996); James Kuklinski et al.,
“‘Just the Facts Ma’am’: Political Facts and Public Opinion,” Annals of the American
Academy of Political and Social Science 560 (November 1998): 143–54; Martin Gilens,
“Political Ignorance and Collective Policy Preferences,” American Political Science Review
95 (June 2001): 379–96.
130 Journal of Democracy
6. Andrea Louise Campbell, How Policies Make Citizens: Senior Political Activism
and the American Welfare State (Princeton: Princeton University Press, 2003); Paul Martin
and Michele Claibourn, “Citizen Participation and Congressional Responsiveness:
New Evidence that Participation Matters,” Legislative Studies Quarterly 38 (February
2013): 59–81.
7. Sarah Kreps and Doug L. Kriner, “The Potential Impact of Emerging Technologies
on Democratic Representation: Evidence from a Field Experiment,” New Media and Society
(2023), https://doi.org/10.1177/14614448231160526.
8. Elena Kagan, “Presidential Administration,” Harvard Law Review 114 (June 2001):
2245–2353.
9. Michael Asimow, “On Pressing McNollgast to the Limits: The Problem of Regulatory
Costs,” Law and Contemporary Problems 57 (Winter 1994): 127, 129.
10. Kenneth F. Warren, Administrative Law in the Political System (New York: Routledge,
2018).
11. Committee on the Status and Future of Federal E-Rulemaking, American Bar Association,
“Achieving the Potential: The Future of Federal E-Rulemaking,” 2008, https://
scholarship.law.cornell.edu/cgi/viewcontent.cgi?article=2505&context=facpub.
12. Jason Webb Yackee and Susan Webb Yackee, “A Bias toward Business? Assessing
Interest Group Influence on the U.S. Bureaucracy,” Journal of Politics 68 (February
2006): 128–39; Cynthia Farina, Mary Newhart, and Josiah Heidt, “Rulemaking vs. Democracy:
Judging and Nudging Public Participation That Counts,” Michigan Journal of
Environmental and Administrative Law 2, issue 1 (2013): 123–72.
13. Edward Walker. “Millions of Fake Commenters Asked the FCC to End Net Neutrality:
‘Astroturfing’ Is a Business Model,” Washington Post Monkey Cage blog, 14 May
2021, www.washingtonpost.com/politics/2021/05/14/millions-fake-commenters-asked-
fcc-end-net-neutrality-astroturfing-is-business-model/.
14. Adam Przeworksi, Susan C. Stokes, and Bernard Manin, eds., Democracy, Accountability,
and Representation (New York: Cambridge University Press, 1999).
15. Report of the Select Committee on Intelligence United States Senate on Russian
Active Measures Campaigns and Interference in the 2016 U.S. Election, Senate Report
116–290, www.intelligence.senate.gov/publications/report-select-committee-intelligence-
united-states-senate-russian-active-measures.
16. On the potentially limited effects of 2016 election misinformation more generally,
see Andrew M. Guess, Brendan Nyhan, and Jason Reifler, “Exposure to Untrustworthy
Websites in the 2016 US Election,” Nature Human Behavior 4 (2020): 472–80.
17. James Vincent, “AI Is Killing the Old Web, and the New Web Struggles to be
Born,” The Verge, 26 June 2023, www.theverge.com/2023/6/26/23773914/ai-large-lan-
guage-models-data-scraping-generation-remaking-web.
18. Josh Goldstein et al., “Can AI Write Persuasive Propaganda?” working paper, 8
April 2023, https://osf.io/preprints/socarxiv/fp87b.
19. Sarah Kreps, “The Role of Technology in Online Misinformation,” Brookings Institution,
June 2020, www.brookings.edu/articles/the-role-of-technology-in-online-misin-
formation.
20. In this way, AI-generated misinformation could greatly heighten “desensitization”—the
relationship between incumbent performance and voter beliefs—undermining
democratic accountability. See Andrew T. Little, Keith E. Schnakenberg, and Ian R. Turn-
131Sarah Kreps and Doug Kriner
er, “Motivated Reasoning and Democratic Accountability,” American Political Science
Review 116 (May 2022): 751–67.
21. Sarah Kreps, R. Miles McCain, and Miles Brundage, “All the News that’s Fit to
Fabricate,” Journal of Experimental Political Science 9 (Spring 2022): 104–17.
22. Kathleen Donovan et al., “Motivated Reasoning, Public Opinion, and Presidential
Approval” Political Behavior 42 (December 2020): 1201–21.
23. Mark Warren, ed., Democracy and Trust (New York: Cambridge University Press,
1999); Robert Putnam, Bowling Alone: The Collapse and Revival of American Community
(New York: Simon and Schuster, 2000); Marc Hetherington, Why Trust Matters: Declining
Political Trust and the Demise of American Liberalism (Princeton: Princeton University
Press, 2005); Pippa Norris, ed., Critical Citizens: Global Support for Democratic
Governance (New York: Oxford University Press, 1999); Steven Levitsky and Daniel
Ziblatt, How Democracies Die (New York: Crown, 2019).
24. Lewis Anthony Dexter, “What Do Congressmen Hear: The Mail,” Public Opinion
Quarterly 20 (Spring 1956): 16–27.
25. See, among others, Pew Research Center, “Public Trust in Government: 1958–
2022,” 6 June 2022, www.pewresearch.org/politics/2022/06/06/public-trust-in-govern-
ment-1958-2022/#:~:text=Only%20two%2Din%2Dten%20Americans,least%20most%20
of%20the%20time.
26. Thomas Patterson, Out of Order (New York: Knopf, 1993); Joseph N. Cappella
and Kathleen Hall Jamieson, “News Frames, Political Cynicism, and Media Cynicism,”
Annals of the American Academy of Political and Social Science 546 (July 1996): 71–84.