Shipping AI means taking responsibility for what it produces and what data it touches. This matters most in sensitive areas such as hiring.
Reduce hallucinations
- Ground answers in supplied sources and require citations.
- Allow "I do not know" as a valid answer.
- Verify facts with code or tools where possible.
- Keep a person in charge of high-stakes decisions.
Protect personal data
- Send the model the minimum data needed. Redact names, phone numbers and IDs where they are not required (PII redaction).
- Know your provider's data retention and training terms, and tell users how their data is used.
- Set retention limits and let users delete their data.
- Keep access logs for audit.
Bias and fairness
Models can reflect biases in their training data. For hiring-related features, avoid using protected traits, test outputs across different groups of people, and never use an AI score as the only reason for a decision about a person.
Content safety
- Moderate inputs and outputs for your context.
- Add rate limits and abuse monitoring.
- Give users a way to report problems.
Copyright and provenance
Know where your data comes from and whether you may use it. Do not present generated text as a source it did not come from.
How Anthropic approaches safety
Anthropic publishes research and policies on safety. Ideas you will see in its documentation include Constitutional AI (training a model to follow a written set of principles), safety evaluations, red-teaming, interpretability research and a Responsible Scaling Policy. Models also refuse some requests by design, and you should handle refusals gracefully in your UI.