Responsible AI practices
Responsible AI
Responsible AI is the set of practices and principles that keep AI systems transparent and trustworthy while mitigating potential risks and negative outcomes. It applies across the whole lifecycle: design, development, deployment, monitoring, and evaluation.
What a responsibly run AI system has
- Transparency and accountability, with monitoring and oversight
- A leadership team accountable for the responsible AI strategy
- Teams with responsible AI expertise
- Development that follows responsible AI guidelines
It applies to all AI
Responsible AI is not only for generative AI.
- Traditional ML models do one task each (ranking, sentiment analysis, image classification) and learn patterns from carefully prepared training data. Examples: recommendation engines, gaming, voice assistants.
- Generative AI runs on foundation models that handle many tasks. It brings business value through creativity, productivity, and connectivity with customers and across the organization.