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Science Technology Innovation Interface Studies

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Science Technology Innovation Interface Studies

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Research Frontiers in Artificial Intelligence Bias Detection and Mitigation

Develops methodologies to identify and reduce algorithmic discrimination in machine learning systems.

Emergent Bias in Multi-Modal Foundation Models
Algorithmic Fairness Under Distribution Shift
Hidden Representation Spaces and Latent Discrimination
Bias Amplification in Cascade Learning Systems
Fairness-Robustness Trade-offs in Adversarial Settings
Demographic Parity Collapse in Transfer Learning
Intersectional Bias Dynamics in Language Models
Counterfactual Fairness in Causal ML Pipelines
Data Labeling Bias and Downstream Model Corruption
Temporal Bias Drift in Continual Learning Systems

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