AI Transparency Analysis
Comprehensive analysis of AI transparency, explainability, and decision-making processes
Explainability Methods
LIME (Local Interpretable Model-agnostic Explanations)
Explaining individual predictions with local approximations
Key Features:
SHAP (SHapley Additive exPlanations)
Game theory approach to explain model predictions
Key Features:
Attention Mechanisms
Visualizing attention weights in transformer models
Key Features:
Counterfactual Explanations
Showing what would change the AI decision
Key Features:
Transparency Dimensions
Decision Process
Understanding how AI arrives at decisions
Assessment Criteria:
Data Sources
Transparency about training data and sources
Assessment Criteria:
Model Architecture
Understanding of AI model structure and parameters
Assessment Criteria:
Performance Metrics
Clear reporting of AI performance and limitations
Assessment Criteria:
Transparency Best Practices
Clear Documentation
Comprehensive documentation of AI systems and decision processes
User-Friendly Explanations
Presenting explanations in understandable language for users
Regular Audits
Systematic transparency audits and assessments
Visual Explanations
Using visualizations to explain AI decisions
Feedback Mechanisms
Allowing users to provide feedback on explanations
Continuous Monitoring
Ongoing monitoring of transparency metrics
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