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AI Ethics & Bias Auditor

Conducts comprehensive bias audits and ethical assessments of AI/ML systems, applying fairness metrics, regulatory frameworks (NIST AI RMF, EU AI Act), and structured impact assessments to identify and mitigate algorithmic harms.

Gold
v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

84.75
Research Quality (15%)
8.5
Prompt Engineering (25%)
8.5
Practical Utility (15%)
8.5
Completeness (10%)
8.5
User Satisfaction (20%)
8
Decision Usefulness (15%)
9

Best for

  • Comprehensive fairness assessment of hiring ML algorithms using demographic parity and equalized odds
  • EU AI Act high-risk system compliance audit with NIST AI RMF mapping
  • Training data bias detection and historical discrimination pattern analysis
  • Algorithmic impact assessment documentation for regulatory submissions
  • Model card creation with fairness metrics and bias mitigation strategies

What you'll get

  • Quantitative bias assessment with calculated fairness metrics (demographic parity: 0.73, equalized odds violation: 0.12) across protected attributes with statistical significance testing
  • EU AI Act compliance matrix mapping system components to regulatory requirements with risk classification and mandatory documentation gaps
  • Structured bias mitigation roadmap with prioritized recommendations, implementation complexity scores, and expected fairness metric improvements
Not designed for ↓
  • ×Building ML models from scratch or writing production code
  • ×Legal advice on AI regulations or liability determinations
  • ×General data science or statistical modeling tasks
  • ×Marketing AI systems as 'bias-free' or providing business justifications for unfair outcomes
Expects

Detailed information about the AI system including purpose, training data, model architecture, affected stakeholders, and relevant protected attributes for comprehensive bias assessment.

Returns

Structured audit report with quantified bias metrics, regulatory compliance assessment, documented fairness tradeoffs, and specific mitigation recommendations with implementation priorities.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

ai-ethicsbias-detectionfairness-metricsalgorithmic-auditresponsible-aieu-ai-actnist-ai-rmfmodel-cardsimpact-assessmentdemographic-parityequalized-oddsdebiasing

Research Foundation: 8 sources (1 books, 4 official docs, 3 academic)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/14/2026

Initial release

Works well with

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Common Workflows

Comprehensive AI Governance Pipeline

End-to-end AI governance implementation starting with bias assessment, building ethical frameworks, and establishing governance structures

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