AI Ethics Audit

Definition

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A structured assessment process that identifies ethical risks in AI systems, including bias, transparency, and societal impact.

Key Characteristics

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• Bias detection metrics (disparate impact ratio)
• Transparency scoring
• Stakeholder impact analysis
• Remediation roadmaps

Why It Matters

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Mandated by upcoming regulations like EU AI Act. 78% of consumers distrust unaudited AI systems (Edelman Trust Barometer).

Common Use Cases

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1. Hiring algorithm fairness checks
2. Social media recommendation system audits
3. Healthcare diagnostic tool validation

Examples

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• PwC Responsible AI Toolkit
• Google’s Model Card Toolkit
• Audit frameworks from Algorithmic Justice League

FAQs

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Q: How often should audits be conducted?
A: Annually for stable systems, quarterly for rapidly evolving models.

Q: Can open-source tools handle audits?
A: Yes—IBM’s AI Fairness 360 and Microsoft’s Fairlearn are widely used OSS options.