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Explainable Ai

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Explainable Ai

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Research Frontiers in Counterfactual Explanation Generation

Methods for generating minimal input modifications that would change a model''s prediction to provide contrastive explanations.

Causal Inference Through Minimal Perturbation Spaces
Contrastive Explanations in High-Dimensional Decision Boundaries
Temporal Counterfactuals in Sequential Decision Models
Feasibility and Plausibility in Synthetic Instance Generation
Counterfactual Robustness Under Distribution Shift
Multi-Modal Counterfactuals Across Heterogeneous Data Types
Actionable Recourse Beyond Model-Agnostic Approximations
Fairness-Aware Counterfactual Paths in Algorithmic Decision Systems
Disentangled Representations for Interpretable Counterfactual Synthesis
Causal Graph Discovery From Counterfactual Query Responses

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