AI incident #659 ·
Purported Mass Facial Recognition Program in Gaza Reportedly Used by Israeli Forces to Identify Palestinians
What happened
A previously undisclosed facial recognition initiative operated by Israeli military intelligence units was reportedly deployed across Gaza after the October 7, 2023 attacks. According to multiple intelligence officers, the program uses Corsight technology alongside Google Photos to identify individuals from checkpoints, crowds, and drone footage. The system has allegedly produced misidentifications, including the widely reported detention of Palestinian poet Mosab Abu Toha on November 19, 2023.
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 (3)
Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.
Who was involved
- Alleged deployer
- Unit 8200 Israeli military intelligence Israeli government Israel Defense Forces Facial recognition system deployers
- Alleged developer
- Unknown Israeli military integrators Surveillance technology developers Google Photos Facial recognition system developers Corsight
- Alleged harmed party
- Privacy and data rights of Gaza residents Privacy Palestinians traveling through Gaza checkpoints Palestinians National security and intelligence stakeholders Mosab Abu Toha General public of Gaza General public Gazans Epistemic integrity Biometric data subjects
AI systems implicated
Unit 8200 facial recognition programSurveillance technologyIsraeli drone-integrated facial recognition systemIsraeli checkpoint facial recognition camerasGoogle Photos face-matching featuresFacial recognition systemsCorsight facial recognitionAutomated Israeli suspect-flagging algorithmAI-enabled decision support systems
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
- 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...
- Privacy Leakage
"Privacy Leakage means the generated content includes sensitive personal information"
- 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...
- 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...
- 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
"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 on the AI Incident Database
Linked by AIID editors or by its text-similarity model.
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