AI incident #581 · 24 June 2023

Google Ads Allegedly Serving Content on AI-Generated Misinformation Sites

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

What happened

Google’s advertising platform, Google Ads, has allegedly been found to be serving ads on AI-generated content farms that often disseminate misinformation. Despite policies prohibiting such practices, reportedly there are approximately 356 out of 393 ads from major brands that were found to be served by Google on these problematic sites. Particularly concerning according to the reporting were instances where Google Ads were found on sites like MedicalOutline.com, which spreads harmful health misi

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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.

26 Jun 2023
Junk websites filled with AI-generated text are pulling in money from programmatic ads
technologyreview.com · Tate Ryan-Mosley · AIID #3244

Who was involved

Alleged deployer: Google
Alleged developer: Google
Alleged harmed party: Subaru, Major Brands Whose Advertisements Were Found On These Sites, Gnc, General Public, Citigroup

Classification (MIT AI Risk Repository taxonomy)

Risk domain: Misinformation
Risk subdomain: 3.1 False or misleading information
Causal entity: AI
Intent: Unintentional
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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