This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
A psychiatrist reported that Facebook's 'People You May Know' feature was recommending her patients to one another as friends. This occurred because the patients had the psychiatrist's phone number saved in their contacts, which the algorithm used to infer social connections. The incident resulted in a breach of patient privacy, as individuals with sensitive medical conditions were identified to other patients in the same practice.
Facebook's "People You May Know" (PYMK) feature was reported by a psychiatrist for recommending her patients as friends through recommendations, violating patients' privacy and confidentiality.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that memorize and leak sensitive personal data or infer private information about individuals without their consent. Unexpected or unauthorized sharing of data and information can compromise user expectation of privacy, assist identity theft, or cause loss of confidential intellectual property.
The AI system inferred sensitive, non-public relationships between psychiatric patients and leaked their identities to one another based on shared contact data.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The privacy breach was caused by the automated decisions of Facebook's recommendation algorithm operating in production, which produced unintended and harmful social connections.
Added categories peak at 2: Minor (Psychological Harm, Indirect)
This incident involves a localized privacy breach where Facebook's autonomous recommendation algorithm exposed patient identities to each other via shared phone contacts. While it highlights critical algorithmic privacy risks for sensitive consumer data, it has negligible national security implications across all core assessment categories.
The recommendation system operates independently using learned patterns and network graph data to generate friend suggestions without real-time human intervention.
Minimal or No Risk: The system is a social media recommendation feature, which generally falls under minimal risk applications as it does not fit the high-risk or unacceptable categories.
The primary function of the 'People You May Know' system is to recommend social connections and facilitate matchmaking/friend-making on the platform.
The report provides a clear, detailed account of the privacy failure, including the mechanism (contact syncing of the psychiatrist's phone number) and the specific real-world consequences for patients.
None that are highly plausible; the contact-syncing explanation is strongly supported by the evidence.
Technology
Context-Insensitive Algorithm
Algorithm treats medical contacts like ordinary social connections.
Implicit Network Mapping
System automatically links users sharing a common contact.
Data Inputs
Imported Contact Lists
Users uploading phone contacts containing the psychiatrist's number.
Shared Phone Number Data
Psychiatrist providing cell phone number to Facebook.
Human Factors
Unawareness of Contact Sharing Risks
Users did not realize contact sharing would link them to other patients.
Overreliance on Default Privacy
Assuming private medical relationships would remain confidential.
Process and Methods
Inadequate Privacy Risk Assessment
Lack of evaluation on how contact matching affects sensitive professions.
Opaque Recommendation Logic
Lack of transparency in how friend recommendations are generated.
Management
Prioritizing Growth Over Privacy
Maximizing network growth and engagement over user data privacy.
Regulatory Environment
Absence of Health Privacy Mandates
Lack of regulations preventing social networks from mapping patient networks.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
