Framework
Pillar 4: Responsible AI
Why Responsible AI matters
AI workloads and their underlying infrastructure, models, and use of data must be reliable and safe in any scenario into which they are deployed…
Responsible AI (RAI) requires ongoing monitoring, correction and tuning. We must either take RAI seriously or walk away from AI altogether.
Reliability and Safety
Reliability and Safety ensure that AI systems are dependable and secure, functioning correctly under diverse circumstances. This involves rigorous testing (we call it “red teaming”) and continuous monitoring to prevent failures and mitigate risks.
Privacy and Security
Privacy and Security focus on protecting individual privacy and ensuring data security. This includes implementing robust security measures and being transparent about data collection, usage, and storage practices.
Fairness and Inclusivity
Fairness and Inclusivity ensure that AI systems treat all people equally and are accessible to diverse populations. This involves identifying and eliminating biases and designing AI technologies that are usable by people from all backgrounds.
Transparency
Transparency involves making AI systems understandable and providing clear information about their operation, how they reason, etc. This fosters trust and accountability by explaining how AI systems work and the data they use.
Accountability
Accountability ensures that organizations are responsible for the outcomes produced by their AI systems. This includes having clear lines of responsibility and mechanisms for addressing issues that arise from AI deployment.