Measuring Generative AI Content in Political Campaigns: Evidence of Rising Adoption and Non-Disclosure

Anna-Maria Gueorguieva; Nic Weber

cs.AINov 8, 2026

Measuring Generative AI Content in Political Campaigns: Evidence of Rising Adoption and Non-Disclosure

Authors: Anna-Maria Gueorguieva, Nic Weber

Organizations: Information School, University of Washington, Seattle, Washington, USA

Abstract

The use of generative AI in political campaigns has drawn public concern and policy attention. Public concerns range from distrust of candidates to skepticism about the authenticity and accuracy of political advertising, including AI-generated images and video. Nearly half of U.S. states have enacted laws that prohibit, or require disclosure of, "materially deceptive" AI-generated content (AIGC) in elections to guard against these concerns. This study measures how candidates and their campaigns produce and circulate AIGC during an election cycle. We develop two measures that estimate not only wether campaigns use AIGC, but how much they produce (magnitude) and in what forms (modality). We apply these measures to a novel dataset of text and images from the campaign websites, X accounts, and emails of 12,977 candidates for U.S. political office from 2023 to 2025. We report three findings. First, detectable AIGC from campaigns increased significantly across modalities over the observation window, and campaigns that use AIGC frequently do not disclose it. Second, incumbents' campaigns produced significantly less AIGC than challengers' campaigns. Third, candidates in states with AIGC disclosure or prohibition laws do not produce measurably less AIGC than candidates in other states. Our method and findings have implications for transparency and trust in elections, and on state laws that regulate AI use in campaigns. Because current state laws largely target mainly "materially deceptive" content, we identify additional policy mechanisms that could increase electoral transparency and improve trust as AIGC becomes more prevalent.

Figures & tables

Figure 1. We collect data on political candidates across political races from 2023 to 2025 and their campaign data from three modalities (campaign websites, X posts, and emails). We apply AI detection methods to text and image content and introduce two metrics to estimate prevalence of AIGC across races and modalities. We use these estimates to characterize detectable AIGC use over time, which types of candidates use it, and potential effects of state regulations on its use.

Variable	Level	Reference	AME	95% CI	$p$	Rel. change	Rel. 95% CI
Incumbency	Incumbent	Challenger	$-0.014$	$[-0.017,-0.010]$	$<0.001$	$-20.0\%$	$[-24.6\%,-15.4\%]$
	Open	Challenger	$-0.005$	$[-0.009,-0.001]$	$0.023$	$-7.4\%$	$[-13.6\%,-1.3\%]$
	Open	Incumbent	$0.009$	$[0.005,0.012]$	$<0.001$	$+15.7\%$	$[+9.0\%,+22.4\%]$
Party	Republican	Democrat	$0.003$	$[0.000,0.006]$	$0.028$	$+5.3\%$	$[+0.5\%,+10.1\%]$
Race type	US Legislator	State Legislator	$0.010$	$[0.005,0.015]$	$<0.001$	$+16.3\%$	$[+7.7\%,+24.9\%]$
Compliance to law	Disclosure	None	$0.003$	$[0.000,0.007]$	$0.044$	$+5.7\%$	$[+0.0\%,+11.4\%]$

Table 2. Average marginal effects on campaign AI magnitude.

Figure 3. Impact of state regulations at the office-level. Each point is the estimated coefficient on the “regulated” indicator from a separate two-way fixed effects (TWFE) regression; The three colors portray differences in how the outcome is defined: Any use (yes/no), green = whether a candidate used any AIGC at all (binary); Amount (all candidates), red = magnitude of AIGC use across all candidates (zeros included); Amount (users only), blue = magnitude of use among only candidates who used some AIGC (zeros excluded).

Research question	Finding	Implication
RQ1: What is the magnitude of detectable AI-generated content in campaigns?	7.6% of text samples (N= 29,996) and 6.1% of image samples (N=104,562) are detected to be AI; candidates rarely disclose AI use.	Enforcement of disclosure and prohibition laws may be difficult given the lack of transparency and monitoring capabilities.
RQ2: How are the features of a campaign associated with the use of AI-generated content?	Challengers have, on average, higher AI magnitude than incumbents .	AIGC is not evenly distributed across elections and candidates, though effects are small.
RQ3: What is the effect of state-level regulations on the use of AI-generated content in campaigns?	Federal candidates use less AIGC when regulated; there is no effect on state or municipal races.	Current AIGC regulations have a narrow and uneven relationship with campaign messaging.

Table 3. A summary of our primary research questions, findings, and implications.

Appendix figures & tables10 assets

Supplementary material from the paper’s appendix.

Appendix

State	Date Enacted	Bill Number	Type	Time frame Requirements	Penalties	Explicitly only about deception?
Texas	11/01/2019	TX BS 751	Prohibition	30 days before	Class A misdemeanor. Max of 1 year in prison and/or a fine of $4,000.	Yes
Michigan	01/01/2023	MI HB 5141	Disclosure	90 days before	First violation: Max of 90 days in prison and/or a fine of $500. Second violations: Max of five years in prison and/or a fine of$ 1,000.	No
Washington	04/13/2023	WA SB 5152	Disclosure	None	A candidate may seek general or special damages, including attorney’s fees and costs.	No
Minnesota	08/01/2023	MN HB 1370	Prohibition	90 days before a nominating convention	First violation: Max of 90 days in prison and/or a fine of $1,000. Second violation: Max of five years in prison and/or a fine of$ 10,000. Violations that result in bodily harm: Max one year in prison and/or a fine of $3,000.	Yes
California	01/01/2024	CA AB 2839	Disclosure	120 days before, 60 days after	A candidate may seek general or special damages, including attorney’s fees and costs.	Yes
Florida	02/09/2024	FL HB 919	Disclosure	None	A person who fails to include the disclaimer is guilty of misdemeanor of the first degree.	Yes

Appendix

Table 4. Summary of all U.S. state regulation for AI-generated content / use in elections enacted from 2019–2025.

Race	Year	Candidates Total	% of Names Found	% Campaign Site Coverage	% Campaign X URL Coverage
Attorney General	2023	6	100.0	100.0	100.0
	2024	20	100.0	100.0	80.0
	2025	2	100.0	100.0	100.0
Governor	2023	6	100.0	100.0	83.3
	2024	22	100.0	100.0	72.7
	2025	4	100.0	100.0	75.0

Appendix

Table 5. Coverage of Candidates by Year and Race Type. Percentage of campaign site and X coverage are calculated from the amount of candidate names we have, not from all candidates. *For 2024, we could find no breakdown of state senate and house; we found 10,098 state legislatures total out of 11,718 and thus generalize to 86% for both senate and house. **For state legislative elections, some general election candidates may be missing and are replaced by primary candidates.

Feature of Campaign	Definition	Source
Race type	The office for which a candidate is running.	Collected while conducting data collection from a range of sources: Ballotpedia, Wikipedia, MIT Election Lab, FollowTheMoney.org (now, OpenSecrets.org)
Election type	Whether or not the election is a special election	Same sources as Race Type. This data is also sourced manually for races that were not available via automated data collecting or external datasets, such as for U.S. Senate 2024 races.
Incumbency status	Whether the candidate was an incumbent (already in office), a challenger to an incumbent, or it was an open race with no sitting incumbents	This data is included on Ballotpedia. For data that we collect manually we also save this information.
Funding	Total contributions the campaign recieved	FollowTheMoney.org (now, OpenSecrets.org)
Race outcome	Whether the candidate won or lost	Collected while conducting data collection from a range of sources: Ballotpedia, Wikipedia, MIT Election Lab, FollowTheMoney.org (now, OpenSecrets.org)
Party	Political party of the candidate. Can be any party, not necessarily Democrat or Republican, though we conduct analysis only on Democrat vs Republican for simplicity.	Collected while conducting data collection from a range of sources: Ballotpedia, Wikipedia, MIT Election Lab, FollowTheMoney.org (now, OpenSecrets.org)

Appendix

Table 6. Description and sources of features of candidate campaigns used in RQ2

	Mayor	State House	State Senate	US House	US Senate	Attorney General	Governor
2023	0.029	0.055	0.024	0.015	NA	0.006	0.046
2024	0.060	0.057	0.055	0.062	0.050	0.057	0.066
2025	0.062	0.076	0.089	0.135	NA	0.003	0.018

Appendix

Table 7. Average magnitude per year and race

Figure 4. Percentage of text content per electoral race across years 2023-2025 identified to be AI

Variable	Level	Reference	AME	95% CI	$p$	Rel. change	Rel. 95% CI
Incumbency	Incumbent	Challenger	$-0.014$	$[-0.018,-0.010]$	$<0.001$	$-20.5\%$	$[-25.1\%,-15.8\%]$
	Open	Challenger	$-0.006$	$[-0.010,-0.001]$	$0.015$	$-8.2\%$	$[-14.5\%,-1.9\%]$
	Open	Incumbent	$0.008$	$[0.005,0.012]$	$<0.001$	$+15.4\%$	$[+8.5\%,+22.4\%]$
Party	Republican	Democrat	$0.003$	$[0.000,0.006]$	$0.028$	$+5.3\%$	$[+0.5\%,+10.1\%]$
Race type	US Legislator	State Legislator	$0.008$	$[0.001,0.014]$	$0.024$	$+12.9\%$	$[+1.6\%,+24.2\%]$
Compliance law	Disclosure	None	$0.003$	$[0.000,0.007]$	$0.041$	$+5.8\%$	$[+0.1\%,+11.4\%]$

Appendix

Table 8. Average marginal effects on campaign AI magnitude, interaction model.

Question	Hypothesis	Estimate	Std. Error	$z$	$p$	S	95% CI
oes the IQR funding effect differ by incumbency?	Incumbent $-$ Challenger	$-0.000683$	0.00154	$-0.442$	0.658	0.6	[ $-0.00371$ , 0.00234]
	Open $-$ Challenger	$-0.002324$	0.00179	$-1.302$	0.193	2.4	[ $-0.00582$ , 0.00118]
	Open $-$ Incumbent	$-0.001641$	0.00177	$-0.929$	0.353	1.5	[ $-0.00510$ , 0.00182]
Does the IQR funding effect differ by race type?	US Legislator $-$ State Legislator	0.00201	0.00212	0.949	0.343	1.5	[ $-0.00214$ , 0.00616]

Appendix

Table 9. Differences in the effect of an interquartile increase in campaign funding, by incumbency status and race type. Estimates are differences in conditional AMEs when moving total funding from 25th percentile to 75th pecentile.

Question	Hypothesis	Estimate	Std. Error	$z$	$p$	S	95% CI
Does the race-type effect differ by incumbency?	Incumbent $-$ Challenger	$-0.00472$	0.00567	$-0.832$	0.406	1.3	[ $-0.0158$ , 0.00640]
	Open $-$ Challenger	$-0.00719$	0.00811	$-0.886$	0.376	1.4	[ $-0.0231$ , 0.00872]
	Open $-$ Incumbent	$-0.00247$	0.00759	$-0.326$	0.745	0.4	[ $-0.0173$ , 0.01240]

Appendix

Table 10. Differences in the effect of race type by incumbency status. Estimates are differences in conditional AMEs of race type (U.S. legislature vs state legislature) across incumbency groups

Figure 5. Raw distribution of AIGC amount (inverse-hyperbolic-sine scale) among candidates in unregulated versus regulated state-cycles, shown separately for federal, state, and municipal offices. Boxes give the median and interquartile range; points are individual candidates.

Office	Outcome	Diff.	95% CI	$n$
Federal	Amount, all	-0.035	$[-0.064,-0.005]$	817
	Any use	$-0.017$	$[-0.047,0.013]$	817
	Amount, users	-0.034	$[-0.065,-0.002]$	803
State	Amount, all	$-0.026$	$[-0.076,0.024]$	6205
	Any use	$0.036$	$[-0.117,0.188]$	6205
	Amount, users	$-0.029$	$[-0.080,0.021]$	5984

Appendix

Table 11. Coefficient on regulated from two-way fixed effects regressions, by office level. Bold marks 95% CIs excluding zero. Estimates are in inverse-hyperbolic-sine units (yes/no for “Any use”). Outcomes: Amount, all

=

AIGC amount over all candidates (non-users

=0

); Any use

=

used any AIGC; Amount, users

=

amount among users only. All models absorb state and year fixed effects and adjust for incumbency, party, log funding, special-election status, and district lean; SEs clustered by state.

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