AI Explainability and Interpretability

Design end-to-end AI transparency frameworks for organizations. Bridge technical explainability outputs with stakeholder communication, governance structures, and user trust.

Visualize and interpret attention patterns in transformer models and LLMs. Identify attention heads, cross-attention structures, and token-level attribution for NLP and vision tasks.

Apply TCAV, concept activation vectors, and concept-based XAI methods to explain deep learning models in human-meaningful terms beyond raw feature attributions.

Design actionable counterfactual and contrastive explanations for ML model decisions. Generate 'what would need to change' outputs for affected users and auditors.

Use feature attribution methods to debug ML models, detect spurious correlations, identify data leakage, and diagnose unexpected model behavior through interpretability.

Write rigorous model cards, algorithmic impact assessments, and XAI documentation for ML systems. Communicate model behavior, limitations, and explanation outputs clearly.

Audit ML models for algorithmic bias, fairness violations, and discriminatory outcomes using established fairness metrics and interpretability frameworks.

Explore mechanistic interpretability, probing classifiers, activation analysis, and circuit-level understanding of deep neural networks and large language models.

Apply SHAP and LIME techniques to explain black-box model predictions. Interpret feature importance, local explanations, and model behavior for any ML pipeline.

Navigate AI explainability requirements under GDPR, EU AI Act, and sector-specific regulations. Align your model documentation and explanation outputs with compliance standards.