Based on the search results, the key principles of responsible AI are:
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Accuracy and reliability: AI systems should be developed to achieve industry-leading levels of accuracy and reliability, ensuring outputs are trustworthy and dependable.
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Accountability and transparency: Clear oversight should be established by individuals over the full AI lifecycle, providing transparency into the development and use of AI systems and how decisions are made. The people who design and deploy an AI system need to be accountable for its actions and decisions, especially as we progress toward more autonomous systems.
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Fairness and human-centric: AI systems should be designed with human oversight and diverse perspectives, and aligned with firm values to mitigate risks of unfair discrimination and harmful bias.
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Safety and ethics: The safety of human life, health, property, and the environment should be prioritized when designing, developing, and deploying AI systems, aligning with ESG principles.
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Security and resilience: Potential cyber threats and vulnerabilities should be mitigated to ensure the robustness and resilience of AI systems.
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Interpretability and explainability: AI systems should be designed to be interpretable, allowing humans to understand their operations and the meaning and limitation of their outputs. Design decisions, development protocols, and alignment with responsible AI principles should be documented.
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Privacy-enhanced and data-governed: AI systems should respect privacy and champion robust data governance.
These principles are essential to creating responsible and trustworthy AI as it moves into mainstream products and services. They are guided by two perspectives: ethical and explainable.
