AI incident #681 · 17 July 2023

Never Back Down Super PAC for Ron DeSantis Uses AI Donald Trump Voice in Attack Ad Against Kim Reynolds

Open on the AI Incident Database 1 news report Snapshot 7 Sep 2026

What happened

A pro-Ron DeSantis super PAC released an ad featuring an AI-generated voice of Donald Trump. The ad, created by Never Back Down, aimed to criticize Trump’s treatment of Iowa Gov. Kim Reynolds. The AI-generated voice was confirmed to be based on Trump's post from his social media. This incident is an example of potential deception in political advertising through AI-generated content.

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News reports (1)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

17 Jul 2023
DeSantis PAC uses AI-generated Trump voice in ad attacking ex-president
politico.com · Alex Isenstadt · AIID #3845

Who was involved

Alleged deployer: Ron Desantis'S Presidential Campaign, Never Back Down
Alleged developer: Never Back Down
Alleged harmed party: Kim Reynolds, Donald Trump

Classification (MIT AI Risk Repository taxonomy)

Risk domain: Misinformation
Risk subdomain: 3.1 False or misleading information
Causal entity: Human
Intent: Intentional
Timing: Post-deployment
Harm level: —
Sectors: —
Countries: —

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 3.1.

Pursuing Consistent Context
"LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Knowledge Gaps
"Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Hallucinations
"LLMs generate nonsensical, untruthful, and factual incorrect content"
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Faithfulness Errors
"The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Defective Decoding Process
In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Untruthful Content
"The LLM-generated content could contain inaccurate information"
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Factuality Errors
"The LLM-generated content could contain inaccurate information" which is factually incorrect
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Noisy Training Data
"Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

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