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Science Technology Society Studies

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Science Technology Society Studies

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Research Frontiers in Artificial Intelligence Explainability and Transparency

Investigates methods for making AI decision-making processes interpretable and establishing accountability mechanisms for automated systems.

Interpretability Gaps in Deep Neural Decision Boundaries
Algorithmic Opacity and Structural Inequality in AI Systems
Counterfactual Explanations as Social Accountability Mechanisms
Black-Box Models in High-Stakes Institutional Decision-Making
Saliency Maps and Human Trust Calibration in AI
Procedural Transparency Versus Outcome Explainability Tensions
Interpretability Debt in Multi-Modal AI Architectures
Stakeholder Pluralism in Designing Explainable Systems
Temporal Dynamics of Model Drift and Explanation Validity
Situated Knowledge and Cultural Contexts of AI Transparency

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