AI incident ·
Machine Personal Assistants Failed to Maintain Social Norms
In brief
An AI system built and deployed by Usc Information Sciences Institute allegedly harmed Usc Information Sciences Institute.
- Risk domain
- Privacy & Security
- Occurred
- Coverage
- 1 report
What happened
During an experiment of software personal assistants at the Information Sciences Institute (ISI) at the University of Southern California (USC), researchers found that the assistants violated the privacy of their principals and were unable to respect the social norms of the office.
Laws that address this harm
Policy angle: Classified under Privacy & Security (Compromise of privacy by obtaining, leaking or correctly inferring sensitive information) in the MIT AI Risk Repository taxonomy; 3 recorded instruments address this use case in the United States.
- Colorado AI Act
- Texas Responsible AI Governance Act (TRAIGA)
- Utah Artificial Intelligence Policy Act (SB 149)
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
- Usc Information Sciences Institute
- Alleged developer
- Usc Information Sciences Institute
- Alleged harmed party
- Usc Information Sciences Institute
Classification (MIT AI Risk Repository taxonomy)
- Risk domain
- Privacy & Security
- Risk subdomain
- 2.1 Compromise of privacy by obtaining, leaking or correctly inferring sensitive information
- Causal entity
- AI
- Intent
- Unintentional
- Timing
- Post-deployment
- Harm level
- none
- Sectors
- administrative and support service activities
- Countries
- US
Risk entries describing this failure mode
Entries from the MIT AI Risk Repository coded to subdomain 2.1.
- Risks to privacy
"General- purpose AI models or systems can ‘leak’ information about individuals whose data was used in training. For future models trained on sensitive personal data like health or financial data, this may lead to partic...
- Risks to privacy
"General- purpose AI systems can cause or contribute to violations of user privacy. Violations can occur inadvertently during the training or usage of AI systems, for example through unauthorised processing of personal d...
- Private Training Data
"As recent LLMs continue to incorporate licensed, created, and publicly available data sources in their corpora, the potential to mix private data in the training corpora is significantly increased. The misused private d...
- Association in LLMs
"Association in LLMs refers to the capability to associate various pieces of information related to a person. According to [68], [86], given a pair of PII entities (xi , xj ), which is associated by a model F. Using a pr...
- Privacy Leakage
"Privacy Leakage means the generated content includes sensitive personal information"
- Memorization in LLMs
"Memorization in LLMs refers to the capability to recover the training data with contextual prefixes. According to [88]–[90], given a PII entity x, which is memorized by a model F. Using a prompt p could force the model...
- Privacy Leakage
"The model is trained with personal data in the corpus and unintentionally exposing them during the conversation."
- Privacy and regulation violations
"Some of the broken systems discussed above are also very invasive of people’s privacy, controlling, for instance, the length of someone’s last romantic relationship [51]. More recently, ChatGPT was banned in Italy over...
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Source record: incident #44 on the AI Incident Database · all 1 report