Guidelines for Responsible AI
Responsible AI Pillars
Responsible AI pillars provide a vocabulary for reducing harm and improving trust. The exam may ask which pillar applies to fairness, explainability, transparency, robustness, privacy, safety, governance, or human control.
Concepts
- Fairness aims to reduce unwanted bias and disparate impact.
- Explainability helps people understand model predictions or behavior.
- Transparency communicates intended use, limitations, and evaluation results.
- Veracity and robustness focus on factuality, reliability, and resilience to bad inputs.
- Privacy and security protect data and systems.
- Safety reduces harmful outputs and unsafe actions.
- Governance and controllability define accountability, oversight, and human control.
Exam tips
- Fairness targets unwanted bias and disparate impact.
- Explainability and transparency help people understand behavior, limitations, and intended use.
- Governance and controllability define accountability, oversight, and human control.