Safety & Ethics
Build responsible AI that respects users, protects against harm, and maintains trust.
Safety Measures
- Content filtering: Block harmful, offensive, illegal content in inputs and outputs
- Rate limiting: Prevent abuse through excessive API usage or automated attacks
- Moderation: Implement human-in-the-loop review for sensitive use cases
- Safety guardrails: Define boundaries—what your AI will and won't do
- Prompt injection defense: Sanitize inputs to prevent users from overriding system prompts
- Output validation: Check generated content against safety policies before showing to users
Bias Detection & Mitigation
Test for bias: Evaluate outputs across demographics (gender, race, age, geography, language)
Diverse training data: Ensure datasets represent all user groups fairly
Regular audits: Continuously monitor for emerging biases in production
Transparency: Disclose limitations and known biases to users
Feedback mechanisms: Allow users to report biased or problematic outputs
Ethical Guidelines
- • Transparency: Clearly disclose when users are interacting with AI
- • Accountability: Take responsibility for AI outputs and their consequences
- • Privacy: Respect user data, comply with regulations, minimize collection
- • Fairness: Treat all users equitably regardless of background
- • Human oversight: Maintain human control over critical decisions
- • Purpose limitation: Use AI only for stated, beneficial purposes
Key Takeaways
- Implement safety measures before launch—content filtering, rate limiting, moderation
- Test for bias across demographics and use cases proactively
- Be transparent about AI limitations and known issues
- Build ethical guidelines into your product from day one