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Data Science

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Data Science

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Research Frontiers in Explainable Artificial Intelligence and Interpretability

Development of methods to make black-box machine learning models transparent and interpretable to stakeholders.

Causal Inference in Black-Box Neural Networks
Adversarial Robustness Through Interpretability Constraints
Counterfactual Explanations at Scale
Neural Concept Hierarchies and Human Semantics
Attention Mechanisms as Faithful Feature Attribution
Interpretability-Performance Trade-offs in Deep Learning
Temporal Reasoning in Recurrent Model Explanations
Distributional Shift Detection via Interpretable Proxies
Faithfulness Verification in Post-hoc Explanations
Multi-Modal Fusion Without Interpretability Loss

All Data Science PhD categories