Editing Responsible Ai (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Building a responsible AI governance framework:

'''1. Pre-development: problem framing'''
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Is AI the right solution? (consider non-AI alternatives)
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Define success: who benefits, who might be harmed?
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Identify high-risk groups and collect stratified data
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Establish fairness definition appropriate for the context
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Document intended use, prohibited uses, and limitations
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'''2. Development: technical controls'''
* Diverse training data with explicit coverage targets
* Bias detection at every stage: data → model → output
* Explainability requirements (SHAP/LIME for tabular; attention for NLP)
* Privacy-preserving training where feasible (differential privacy, federated learning)
* Adversarial testing before deployment

'''3. Deployment: governance controls'''
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Model Card publication
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[Risk level classification → appropriate oversight level]
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[Human review requirements for high-stakes decisions]
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[Appeal and redress mechanism for affected individuals]
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[Incident response plan for AI failures]
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[Sunset plan: when will the system be decommissioned?]
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'''4. Ongoing monitoring'''
* Continuous bias metrics dashboard
* User feedback channels for harm reporting
* Quarterly fairness audits with public reports
* External red team engagements annually

[[Category:Artificial Intelligence]]
[[Category:AI Safety]]
[[Category:Ethics]]
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