AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
H
Hallucination
When an AI model generates information that sounds plausible and confident but is factually incorrect, fabricated, or not grounded in its training data or provided context. The model essentially 'makes things up'.
Hallucination (AI)
When a generative AI model produces outputs that are factually incorrect, fabricated, or inconsistent with reality, while presenting them with apparent confidence. Hallucinations are an inherent property of how language models generate text — they produce statistically plausible sequences, not verified facts.
Hallucination Detection
Methods and systems for automatically identifying when an AI model has generated false or unsupported information. Detection can compare outputs against source documents or use consistency checks.
Hallucination Rate
The frequency at which an AI model generates incorrect or fabricated information. It is typically measured as a percentage of responses containing hallucinations.
Hardware Acceleration
Using specialized hardware (GPUs, TPUs, FPGAs, ASICs) to speed up AI computation compared to general-purpose CPUs. Accelerators are optimized for the specific math operations used in neural networks.
High-Risk AI System
Under the EU AI Act, an AI system used in sensitive domains — critical infrastructure, education, employment, essential services, law enforcement, migration, or the administration of justice — that must meet strict requirements for risk management, data governance, transparency, human oversight, and accuracy before market deployment.
Homomorphic Encryption
A form of encryption that allows computation on encrypted data without decrypting it first. The results, when decrypted, match what would have been computed on the plaintext.
HUDERIA
The Council of Europe's Human Rights, Democracy, and the Rule of Law Assurance Framework for AI Systems. It provides guidelines to ensure that AI systems align with fundamental rights, democratic principles, and legal norms established in the European Convention on Human Rights.
Hugging Face
The leading open-source platform for sharing and discovering AI models, datasets, and applications. Hugging Face hosts the Transformers library and a community hub with thousands of pre-trained models.
Human Evaluation
Using human judges to assess AI model quality on subjective dimensions like helpfulness, coherence, creativity, and safety that automated metrics cannot fully capture.
Human-in-Command (HIC)
A governance principle where humans retain ultimate authority and control over AI systems, including the ability to decide the scope of AI autonomy, override any AI decision, modify the system's behavior, and shut it down entirely. HIC is the overarching principle that encompasses both HITL and HOTL as implementation patterns.
Human-in-the-Loop
A system design where humans are integrated into the AI workflow to provide oversight, make decisions, correct errors, or handle edge cases that the AI cannot reliably manage alone.
Human-in-the-Loop (HITL)
A system design pattern where a human reviews and approves every AI output before any action is taken. HITL provides the maximum level of human oversight but constrains the system's speed and scalability to the pace of human review.
Human-on-the-Loop (HOTL)
A system design pattern where AI operates autonomously but a human monitors outputs and retains the ability to intervene, override, or shut down the system when needed. HOTL balances automation efficiency with human oversight for systems where reviewing every output isn't feasible.
Hybrid Search
A search approach that combines keyword-based (lexical) search with semantic (vector) search to get the benefits of both — exact matching for specific terms and meaning-based matching for conceptual queries.
Hyperparameter
Settings that are configured before training begins and control how the model learns, as opposed to parameters which are learned during training. Examples include learning rate, batch size, and number of layers.
Hyperparameter Tuning
The process of systematically searching for the best combination of hyperparameters for a model. Since hyperparameters are set before training, finding optimal values requires experimentation.