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Digital Society Socio Technical Systems

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Digital Society Socio Technical Systems

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Research Frontiers in Algorithmic Fairness and Bias Mitigation

Investigates methods to detect, measure, and reduce discriminatory outcomes in machine learning systems across social domains.

Intersectional Bias Amplification in Cascading Recommendation Systems
Fairness-Robustness Tradeoffs Under Adversarial Distribution Shifts
Temporal Drift of Algorithmic Bias in Production Environments
Fairness Debt: Legacy Bias in Inherited Data Pipelines
Measuring Discrimination in Opaque Black-Box Algorithms
Causal Pathways of Bias in High-Stakes Allocation Systems
Fairness as Contested Values in Socio-Technical Design
Federated Learning and Emergent Group-Level Discrimination
Debiasing Synthetic Training Data for Protected Populations
Auditing Fairness Claims in Commercial AI Governance

All Digital Society & Socio-Technical Systems PhD categories