Miscellaneous
Data Privacy
Data privacy in AI involves ensuring training data, user interactions, and model outputs comply with regulations like GDPR, CCPA, and HIPAA. Key concerns include the use of personally identifiable information (PII) in training, data retention policies, and user rights to deletion and explanation.
AI-specific privacy challenges include membership inference attacks (determining if a specific record was in training data), model inversion (reconstructing training data from model outputs), and unintended PII memorization in LLMs.
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Related Terms
Core Concepts
Bias
Preconceived notions in AI models that affect decision-making and fairness.
Techniques & Methods
AI Alignment
The research field and engineering practice of building AI systems that reliably pursue goals humans actually want, remain controllable, and avoid harmful side effects — operationalized through RLHF, Constitutional AI, evaluations, and interpretability.
Miscellaneous
Training Data
The labeled or unlabeled dataset used to fit a model's parameters during the learning process.
Miscellaneous
Deployment
The process of making a trained AI model available for real-world use in production environments.