AI Safety & Red-Teaming Playbook (Testing Behaviors & Failure Modes)
- Practitioner
- Advanced
- Template Included
A framework for AI safety and red-teaming — deliberately testing AI systems for adversarial inputs, edge-case failure modes, and unintended behaviors before deployment — treating adversarial testing as a distinct, necessary discipline from standard accuracy validation, since standard testing rarely surfaces the failure modes red-teaming specifically targets.
Isn't standard accuracy testing sufficient to validate an AI
system's safety before deployment? Standard accuracy testing validates typical-case performance, but doesn't systematically probe for adversarial inputs, edge cases, or unintended behaviors a motivated user or attacker might discover — red-teaming is a distinct discipline specifically designed to surface these failure modes standard testing misses.
Who should conduct AI red-teaming — the same team that built the
model, or an independent group? Independent red-teaming, ideally from a team not invested in the model's success, tends to surface more genuine failure modes than self-testing by the building team, who may have blind spots about their own system's weaknesses.
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