Responsible AI (RAI) is the practice of building and operating AI systems while managing their potential harms. For an enterprise generative AI application, this means setting policies that reflect company values and addressing bias, privacy, security, misleading content, misuse, and the effects of automation. It is an ongoing responsibility, not a check performed just before launch. section 1.7 section 1.9
Use a four-stage lifecycle to turn those concerns into work the team can repeat:
- Identify potential harms through analysis, stress testing, and red-teaming. section 13.3
- Measure how often harms occur and how severe they are, using evaluation criteria, test sets, and repeatable automated tests. section 13.3
- Mitigate harms with measures such as prompt engineering and postprocessing content filters; rerun evaluations to check the effect. section 13.3
- Operate with a deployment and readiness plan, stakeholder reviews, and ways to respond when new harms appear. section 13.3 section 13.3.4
Enterprise chatbot
An enterprise chatbot needs relevant, high-quality company data, but that data must be managed for privacy and legal compliance before use. Its response-processing layer can screen outputs for bias and appropriateness and apply content moderation. These controls sit alongside governance policies and continued testing; a filter alone is not a responsible AI program. section 1.9 section 10.6
Production operation also needs a phased rollout, user feedback, incident response, and a rollback plan. When an unexpected harm is reported, teams need a way to stop problematic prompts or responses, investigate, and improve the system. Evaluation tools have limitations, so measurement and mitigation must continue after deployment. section 13.3.4 section 13.3
