Here are some basic principles of responsible AI based on the search results:
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Fairness: AI initiatives should strive to incorporate fairness and minimize bias. Design AI systems with human oversight and diverse perspectives, and aligned with firm values to mitigate risks of unfair discrimination and harmful bias.
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Accountability and responsibility: AI initiatives should incorporate an operating model that identifies various roles and stakeholders who are accountable, responsible, provide oversight, or conduct due diligence and verification at various stages of implementing AI projects. Organizations should consider establishing an internal review body that provides oversight, insights, and guidance about developing and deploying AI systems.
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Interpretability and explainability: AI initiatives should account for the maximum feasible level of explainability. AI-based decisions and actions should be explainable to all relevant stakeholders. Interpretable and explainable AI promotes trust and drives informed decisions. Design AI systems to be interpretable, allowing humans to understand their operations and the meaning and limitation of their outputs. Document design decisions, development protocols, and alignment with RAI principles.
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Accuracy and reliability: Develop AI systems to achieve industry-leading levels of accuracy and reliability, ensuring outputs are trustworthy and dependable.
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Safety and ethics: Prioritize the safety of human life, health, property, and the environment when designing, developing, and deploying AI systems, aligning with ESG principles.
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Privacy and security: AI initiatives should ensure the robustness and resilience of AI systems. Mitigate potential cyber threats and vulnerabilities to ensure the privacy and security of AI systems.
Organizations should establish clear principles for the responsible use of AI and commit to adhering to these guiding principles. These principles are essential to creating responsible and trustworthy AI as it moves into mainstream products and services.
