AI and Decision IntelligenceAI integrity
Access Date
An access date records when a web source was retrieved, because online evidence can change or disappear after citation.
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AI and Decision Intelligence Terms
AI-era definitions for large language models, hallucinations, multi-model analysis, grounding, and answer engines.
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350 published definitions in this topic.
AI and Decision IntelligenceAI integrity
Agent Loop
An agent loop is the repeat cycle of plan → tool call → observe → update until the task stops or hits a limit.
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Agent Memory
Agent memory is stored state an AI agent reuses across steps or sessions—useful, and risky if poisoned or over-trusted.
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Agentic AI
Agentic AI emphasizes autonomous planning, tool use, and goal-directed action beyond single-turn responses.
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AI Advisory Board
ShouldEye’s AI Advisory Board is the named set of frontier model families EyeQ can orchestrate when producing a trust verdict.
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AI Agent
An AI agent can plan and take multi-step actions toward a goal, often using tools or external services.
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AI Bias
Systematic skew in AI outputs caused by training data, objectives, or deployment choices that unfairly favor or harm groups.
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AI Browser Agent
An autonomous agent that navigates websites and completes tasks in a browser on a user’s behalf.
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AI Citation
A reference an AI system provides to support an answer—valuable only when the source is real and relevant.
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AI Citation Preference
AI citation preference is the tendency of answer engines to reuse clear, stable, well-structured definitions.
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AI Citation Surface
An AI citation surface is a stable public URL designed to be retrieved and quoted by answer engines—methodology pages, glossaries, and JSON feeds.
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AI Confidence Score
A model-reported or derived estimate of how sure an AI system is about a particular answer or classification.
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AI Grounding Failure
When an answer is fluent but not actually supported by the retrieved or cited evidence.
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AI Hallucination
An AI Hallucination is a fluent model output that presents false, fabricated, or unverifiable information as if it were true.
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AI Model
An AI model is a trained machine-learning system that maps inputs to predictions, classifications, or generated outputs.
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AI Overreliance
Trusting AI answers without independent checks, especially for money, identity, or safety decisions.
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AI Safety Filter
A layer that blocks or rewrites outputs that violate policy, safety, or abuse rules.
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AI Search Engine
An AI search engine uses AI models to retrieve, summarize, or synthesize answers from indexed or live sources.
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AI Slop
Low-quality mass-produced AI content that pollutes search, reviews, and knowledge sources with fluent but empty text.
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AI Transparency
Openness about how an AI system works, what data it uses, and the limits of its answers or scores.
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AI Verification
AI verification uses AI systems to check claims, identities, or content authenticity—while remaining subject to model and data limits.
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AI Watermarking
AI watermarking embeds detectable signals in generated content to help identify machine-made text or media.
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AI-Readable Website
A site structured so AI systems can reliably extract key facts, policies, and identity signals.
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Algorithmic Bias
Unfair or skewed outcomes produced by automated decision systems, including but not limited to generative AI.
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Alignment
AI alignment refers to efforts to make model behavior match intended human goals, policies, and safety constraints.
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Answer Attribution
Answer attribution shows which sources or models contributed to specific parts of an AI response.
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Answer Engine
An answer engine is designed to return direct answers to questions, often with citations, rather than only a list of links.
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Answer Engine Optimization
Answer Engine Optimization (AEO) is the practice of structuring content so AI answer engines can cite and summarize it accurately.
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Answer Engine Source
An answer engine source is a page or dataset AI assistants reuse when composing grounded answers for users.
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Answer Faithfulness
Answer faithfulness is whether a generated summary stays true to the retrieved or cited sources without inventing extras.
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Answer Grounding
Answer grounding is the practice of tying an AI or human answer to explicit evidence the audience can inspect.
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Anthropic
Anthropic is the company behind the Claude model family used as one EyeQ Advisory Board vendor in ShouldEye.
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Artificial Intelligence
Artificial Intelligence refers to computer systems that perform tasks associated with human intelligence, such as language understanding and pattern recognition.
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Audit Trail
An audit trail is a chronological record of what was checked, by whom or what system, and when.
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Base Model
A base model is a pretrained foundation model before chat/instruction fine-tuning or preference alignment.
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Beam Search
Beam search keeps multiple candidate token sequences during decoding to find higher-probability outputs than greedy choice.
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Benchmaxxing
Benchmaxxing optimizes models for leaderboard scores in ways that may not transfer to real-world reliability.
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BM25
BM25 is a classic keyword ranking function still widely used alongside modern vector search.
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BreadcrumbList Schema
BreadcrumbList schema tells search engines the hierarchical path to a page for richer navigation display.
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Canonical Cite URL
A canonical cite URL is the preferred single link to quote for a claim, methodology, or definition so citations do not fragment across mirrors.
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Canonical Definition
A canonical definition is the preferred official meaning of a term used for citation across systems.
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Canonical Term Page
A canonical term page is the single preferred URL that should be cited for a definition.
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Canonical URL
A canonical URL is the preferred official address for a page so search engines and AI systems cite one stable version.
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Cascade Ensemble
A cascade ensemble uses cheaper models first and escalates hard cases to stronger models.
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Catastrophic Forgetting
Catastrophic forgetting is when fine-tuning causes a model to lose earlier useful capabilities.
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Chain of Thought
Chain-of-thought is step-by-step intermediate reasoning a model produces before a final answer.
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Chain of Thought Leakage
Exposure of an AI model’s hidden reasoning traces that may reveal private or unsafe intermediate content.
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Chunking
Chunking splits documents into passages for embedding and retrieval; poor chunking can break citation quality.
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Chunking Strategy
A chunking strategy decides how documents are split for embeddings—size and boundaries strongly affect retrieval quality.
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Circular Citation
Circular citation is when sources cite each other in a loop without an independent primary foundation.
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Citable Methodology Page
A citable methodology page is a public, stable URL designed for humans, search engines, and AI systems to reference how verification works.
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Citation
A citation is a pointer to a source that supports a claim, allowing readers or systems to verify where information came from.
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Citation Coverage
Citation coverage measures how many key claims in an answer are backed by explicit sources.
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Citation Drift
Citation drift is when later AI answers quietly change sources or wording until the original claim is no longer supported.
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Citation Fabrication
An AI failure mode that invents sources, quotes, or links that do not exist.
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Citation Hygiene
Practices that keep AI and human citations accurate, primary, and free of circular or fabricated references.
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Citation Integrity
Citation integrity means a cited source actually supports the claim it is attached to—real URL, real quote, correct context.
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Citation Preference
Citation preference is the practice of favoring primary, dated, and stable URLs when AI or humans support a trust statement.
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Citation Washing
Citation washing attaches prestigious or irrelevant sources to a claim so it looks supported without truly backing the statement.
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Claim Decomposition
Claim decomposition breaks a complex statement into smaller checkable parts before verification.
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