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Science Technology Society Studies

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Science Technology Society Studies

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Research Frontiers in Algorithmic Bias and Fairness in Machine Learning

Examines how algorithmic decision-making systems perpetuate discrimination and develops frameworks for detecting and mitigating bias in AI systems.

Fairness Paradoxes at the Human-Algorithm Interface
Structural Inequality Embedded in Training Data Lineages
Algorithmic Opacity and Contested Fairness Definitions
Bias Amplification Across Model Deployment Lifecycles
Justice Frameworks in Automated Decision-Making Systems
Intersectional Discrimination in High-Stakes ML Applications
Epistemic Authority and Algorithmic Auditing Legitimacy
Proxy Variables and Hidden Discrimination Pathways
Accountability Gaps in Federated and Decentralized Learning
Temporal Drift of Fairness Guarantees in Production Systems

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