AI incident #609 · 16 August 2023

Flawed AI in Google Search Reportedly Misinforms about Geography

Open on the AI Incident Database 2 news reports Snapshot 7 Sep 2026

What happened

Google's search AI erroneously claimed no African country begins with 'K', along with various other geography-and-letter-based questions, misguiding users with a flawed featured snippet. Originating from ChatGPT-written posts and inaccurately scraped by Google, this incident highlights issues in AI-generated content and misinformation in search results, compromising Google's reliability as an information source.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (2)

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

6 Nov 2023
AI Search Is Turning Into the Problem Everyone Worried About
theatlantic.com · Caroline Mimbs Nyce · AIID #3350
16 Nov 2023
Google’s Search AI Is Absolutely Horrible at Geography
futurism.com · Maggie Harrison · AIID #3351

Who was involved

Alleged deployer: Google
Alleged developer: Google, Chatgpt
Alleged harmed party: General Public, Google Users

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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