Responsible AI principles
Fairness
Ensure AI systems treat all people fairly, mitigating bias throughout the model lifecycle. Use diverse data, evaluate for disparate impact, and incorporate fairness‑aware algorithms.
Reliability & Safety
Build models that behave predictably in a variety of conditions. Conduct rigorous testing, monitor performance, and implement fail‑safe mechanisms to protect users.
Privacy & Security
Protect data confidentiality and integrity. Apply encryption, differential privacy, and secure model deployment practices to safeguard user information.
Transparency
Provide clear explanations of model behavior, data sources, and decision logic. Use interpretability tools and documentation to make AI outcomes understandable.
Accountability
Establish governance structures and audit trails. Assign responsibility for AI decisions and ensure mechanisms for redress and continuous improvement.
Inclusiveness
Design AI experiences that are accessible and considerate of diverse user needs, cultures, and contexts.
Environmental Sustainability
Minimize the carbon footprint of AI workloads by optimizing compute resources and leveraging efficient training and inference techniques.