AI UX Safety and Trust Glossary
Failure modes, guardrails, and human oversight patterns that keep AI-assisted experiences reliable and accountable.
8 terms
- Safety and trust
Capability Disclosure
Capability disclosure is the practice of telling users—in plain language—what an AI feature can and cannot do before and during use.
- Safety and trust
Explainability
Explainability is how clearly an AI product shows why it produced an answer: sources, reasoning steps, confidence, or feature influence.
- Safety and trust
Guardrails
Guardrails are rules, filters, and policies that block unsafe inputs, limit risky outputs, and keep AI behavior aligned with product and brand standards.
- Safety and trust
Hallucination
A hallucination is when an AI states something confidently that is false, outdated, or unsupported by its inputs.
- Safety and trust
Human-in-the-Loop
Human-in-the-loop (HITL) means a person reviews, approves, or corrects AI output before it affects users, records, or systems.
- Safety and trust
Moderation
Moderation is the process of detecting and handling harmful, abusive, off-brand, or policy-violating content in AI inputs and outputs.
- Safety and trust
Prompt Injection
Prompt injection is when untrusted text tricks the model into ignoring its instructions or exposing hidden system behavior.
- Safety and trust
Slop (AI Slop)
AI slop is low-quality, generic, or misleading AI-generated content flooding feeds, search results, and products: templated articles, fake thumbnails, and plausible but empty copy.