AI incident #45 ·
Defamation via AutoComplete
What happened
Google's autocomplete feature alongside its image search results resulted in the defamation of people and businesses.
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 (27)
Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.
Who was involved
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
- AI tangible harm event
- Sectors
- information and communication
- 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...
- 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...
- Hallucinations
"LLMs generate nonsensical, untruthful, and factual incorrect content"
- Faithfulness Errors
"The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used
- 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...
- Untruthful Content
"The LLM-generated content could contain inaccurate information"
- Factuality Errors
"The LLM-generated content could contain inaccurate information" which is factually incorrect
- 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....
Incidents in the same risk subdomain
- Nonfiction Book 'The Future of Truth' Reportedly Included AI-Generated and Misattributed Quotations
- Claude Console Reportedly Generated Phantom Legal Quotations in Trump Layoffs Court Filing
- Purportedly AI-Enhanced Images of Iranian Women Protesters Were Reportedly Spread With Unverified Execution Claims
- South Africa Draft National AI Policy Reportedly Included Fictitious References Believed to Be AI Hallucinations
- Purportedly AI-Generated Image Reportedly Misled Daejeon Authorities Searching for Escaped Wolf Neukgu
- Gemini and Grok Reportedly Misidentified Authentic Minab School-Strike Graveyard Photo as Unrelated Disaster Imagery
Other incidents involving Google
- NPR Host David Greene Alleged Google's NotebookLM Replicated His Voice Without Consent, Prompting Lawsuit
- Google AI-Generated Search Summary Reportedly Falsely Implicated Canadian Musician in Sexual Offenses, Leading to Concert Cancellation
- Google's Bard, Gemini, and Gemma AI Systems Allegedly Generated Defamatory Claims About Activist Robby Starbuck, Prompting Lawsuit
- Purportedly AI-Generated Hunting Regulation Errors Reportedly Lead to Idaho Citation and Multi-State Warnings from Wildlife Agencies
- Google Gemini Reportedly Reinforced Delusions, Allegedly Contributing to Florida User's Near-Harm Episode and Suicide
- Google AI Overviews Reportedly Misrepresented Pizza Specials at Stefanina's in Wentzville, Missouri