AI incident ·
Local South Korean Government’s Use of CCTV Footage Analysis via Facial Recognition to Track COVID Cases Raised Concerns about Privacy, Retention, and Potential Misuse
In brief
An AI system built by Unknown and deployed by Bucheon city government allegedly harmed Privacy, General public of South Korea and 3 others.
- Risk domain
- Privacy & Security
- Occurred
- Coverage
- 1 report
What happened
Bucheon government's use of facial recognition in analyzing CCTV footage, despite gaining wide public support, was scrutinized by privacy advocates and some lawmakers for collecting data without consent, and retaining and misusing data beyond pandemic needs.
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; 5 recorded instruments address this use case.
- Colorado AI Act
- India DPDP Act
- 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
- Bucheon city government
- Alleged developer
- Unknown
- Alleged harmed party
- Privacy General public of South Korea General public Bucheon citizens Biometric data subjects
AI systems implicated
Surveillance technologyFacial recognition systemsBucheon COVID-19 facial recognition systemBiometric data processing systemsAI identification and tracking system
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
- Human
- Intent
- Intentional
- Timing
- Post-deployment
- Harm level
- —
- Sectors
- —
- Countries
- —
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...
Related incidents
Linked by editors or by text similarity in the source dataset.
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Source record: incident #276 on the AI Incident Database · all 1 report