Breaking AI Boundaries: Fairness Metrics in Unstructured Data Domains

Unstructured data (images, audio, text) requires different approaches for fairness evaluation compared to structured/tabular data

Key components for evaluating model fairness:

• Finding meaningful data subgroups/clusters
• Measuring model performance across different groups
• Interactive analysis of results
• Automated detection of potential issues

Two main approaches for analyzing unstructured data:

• Model embeddings (using pre-trained models)
• Extracting interpretable features (age, gender, etc.)

Recommended workflow:

• Generate data representations
• Apply dimensionality reduction (PCA, UMAP)
• Use hierarchical clustering
• Measure metrics across clusters
• Analyze problematic clusters interactively

Tools mentioned:

• SliceGuard: Automated detection of problematic data slices
• Spotlight: Interactive data exploration
• Hugging Face Model Hub: Pre-trained models
• EffectNet: Feature extraction

Fairness considerations extend beyond human-centric applications to industrial use cases (automotive testing, machine maintenance)

Interactive analysis is crucial for:

• Understanding why models fail
• Finding patterns in problematic clusters
• Getting actionable insights for improvement

Mitigation strategies are highly use-case specific and may include:

• Data rebalancing
• Label correction
• Additional data collection
• Model adjustments

Challenges include:

• Reliable feature extraction
• Bias in initial representations
• Time-consuming analysis process
• Interpretation of large datasets