「responsible-ai」の検索結果

Implement safety guardrails for AI systems — content filtering, prompt injection detection, output validation, bias mitigation, and responsible AI practices. Use when tasks involve adding safety layers to LLM applications, detecting prompt injection attacks, filtering harmful content, implementing rate limiting for AI APIs, validating LLM outputs against schemas, building moderation pipelines, or ensuring AI systems comply with safety policies.

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Designs LLM, agent, responsible-AI, and multimodal evals. Use when measuring quality, fairness, grounding, safety, or judge reliability. Not for test harnesses (qa-agent-testing).

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when defining AI governance policies, designing human oversight workflows, documenting bias mitigation strategies, or meeting regulatory transparency requirements for Salesforce AI features. Trigger keywords: responsible AI, AI bias, AI audit trail, AI transparency, human-in-the-loop, AI risk inventory, AI disclosure. NOT for the MLOps pipeline or Policy-as-Code engine architecture — use architect/ai-governance-architecture. NOT for Trust Layer technical configuration — use agentforce/einstein-trust-layer.

日本語の概要は準備中です。原文の説明を表示しています。

Expert en éthique de l'IA (bias detection, fairness, explainability, responsible AI, guardrails)

日本語の概要は準備中です。原文の説明を表示しています。

Expert en éthique des agents IA (tests de biais, équité, transparence, explicabilité, déploiement responsable)

日本語の概要は準備中です。原文の説明を表示しています。