Responsible AI Development Practice

A Responsible AI Development Practice is an AI development practice that can be used to create responsible AI systems (that support responsible AI deployment tasks).

• AKA: Ethical AI Development Practice, AI Development Ethics Practice, Responsible AI Practice.
• Context:
  • It can typically implement Responsible AI Development Principles through responsible AI development guidelines and responsible AI development standards.
  • It can typically ensure Responsible AI Development Accountability through responsible AI development documentations and responsible AI development audit trails.
  • It can typically address Responsible AI Development Ethical Considerations through responsible AI development review processes and responsible AI development impact assessments.
  • It can typically establish Responsible AI Development Governance through responsible AI development oversight structures and responsible AI development decision frameworks.
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  • It can often mitigate Responsible AI Development Bias through responsible AI development fairness techniques and responsible AI development diversity measures.
  • It can often promote Responsible AI Development Transparency through responsible AI development explainability methods and responsible AI development disclosure protocols.
  • It can often facilitate Responsible AI Development Stakeholder Engagement through responsible AI development consultation processes and responsible AI development feedback mechanisms.
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  • It can range from being a Basic Responsible AI Development Practice to being a Mature Responsible AI Development Practice, depending on its responsible AI development maturity level.
  • It can range from being a Compliance-Driven Responsible AI Development Practice to being a Values-Driven Responsible AI Development Practice, depending on its responsible AI development motivation source.
  • It can range from being a Project-Specific Responsible AI Development Practice to being an Organization-Wide Responsible AI Development Practice, depending on its responsible AI development implementation scope.
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  • It can integrate with AI Development Lifecycle Stages for responsible AI development continuous integration.
  • It can support AI Ethics Committees for responsible AI development governance oversight.
  • It can align with AI Regulation Frameworks for responsible AI development compliance assurance.
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• Examples:
  • Responsible AI Development Organization Practices, such as:
    • Google AI Principles Practice (2018) implementing responsible AI development ethical guidelines.
    • Microsoft Responsible AI Practice (2019) ensuring responsible AI development fairness standards.
    • IBM AI Ethics Board Practice (2018) providing responsible AI development governance oversight.
    • DeepMind Ethics & Society Practice (2017) advancing responsible AI development research integration.
  • Responsible AI Development Process Implementations, such as:
    • Responsible AI Development Impact Assessment Process evaluating responsible AI development societal consequences.
    • Responsible AI Development Bias Audit Process identifying responsible AI development fairness issues.
    • Responsible AI Development Stakeholder Consultation Process gathering responsible AI development community feedback.
  • Responsible AI Development Tools, such as:
    • Google Model Cards Practice documenting responsible AI development model characteristics.
    • IBM AI Fairness 360 Practice detecting responsible AI development bias patterns.
    • Microsoft InterpretML Practice explaining responsible AI development model decisions.
  • ...
• Counter-Examples:
  • Rapid AI Prototyping Practice, which prioritizes AI development speed over responsible AI development considerations.
  • AI Performance-Only Development Practice, which focuses on AI capability metrics without responsible AI development principles.
  • Traditional Software Development Practice, which lacks responsible AI development specific requirements.
  • AI Research Practice, which emphasizes AI theoretical advancement over responsible AI development implementation.
• See: AI Development Practice, AI Ethics Framework, AI Governance Practice, AI Safety Framework, AI Development Methodology, Ethical Software Development Practice, AI Compliance Framework.

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