Term
- Guardrails (AI)
Definition
Guardrails in AI are safety features that set boundaries or limits on how an AI operates to prevent harmful or inappropriate decisions or outputs.
Where you’ll find it
These settings are usually found in the security or management settings of an AI platform, and they can vary depending on the specific AI system being used.
Common use cases
- Preventing an AI from generating offensive or discriminatory content.
- Limiting AI from accessing sensitive data beyond its necessary scope.
- Ensuring AI outputs do not violate privacy laws or company policies.
Things to watch out for
- Over-restricting AI with guardrails might limit its effectiveness or ability to learn from diverse data.
- Finding the right balance in guardrail settings can be challenging without specific technical guidance.
- Users may need to regularly update and review guardrails to align with changing laws and ethical standards.
Related terms
- AI ethics
- Machine learning security
- Data privacy
- Bias detection
- Compliance management
Pixelhaze Tip: When setting up guardrails, start with strict limits and gradually loosen them as you gain more confidence in how your AI behaves within those boundaries. This approach helps prevent unintended outcomes while you fine-tune the AI's operational scope.