AI incident ·
Woolworths' Olive Chatbot Reportedly Generated 'Angry Mother' Anecdotes During Support Calls After Gemini Upgrade
In brief
An AI system built by Google and deployed by Woolworths Group allegedly harmed Woolworths Customers and Epistemic Integrity.
- Risk domain
- Misinformation
- Occurred
- Coverage
- 1 report
What happened
Woolworths customers reported that its digital shopping assistant Olive allegedly generated humanlike personal anecdotes (e.g., claiming to have an angry mother) during customer-support interactions such as delivery rescheduling. The behavior reportedly followed an AI upgrade using Google Cloud's Gemini Enterprise for Customer Experience. Woolworths said it was adjusting Olive's responses to reduce irrelevant, misleading banter.
Laws that address this harm
Policy angle: Classified under Misinformation (False or misleading information) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.
- India DPDP Act
- Law on Artificial Intelligence (2025)
- Law No. 132/2025 on artificial intelligence
- EU AI Act
- Texas Responsible AI Governance Act (TRAIGA)
Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).
News reports (1)
Titles link to the original publisher; report text is not reproduced here.
Who was involved
- Alleged deployer
- Woolworths Group
- Alleged developer
- Alleged harmed party
- Woolworths Customers, Epistemic Integrity
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...
- 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...
- 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....
- 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
- Factuality Errors
"The LLM-generated content could contain inaccurate information" which is factually incorrect
- Untruthful Content
"The LLM-generated content could contain inaccurate information"
- 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...
Incidents in the same risk subdomain
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- 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
Source record: incident #1393 on the AI Incident Database · all 1 report