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Digital Ethics Society

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Digital Ethics Society

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Research Frontiers in Algorithmic Bias in Hiring Systems

Examining how machine learning models perpetuate discrimination in recruitment processes and developing mitigation strategies.

Proxy Discrimination: Hidden Pathways in Algorithmic Selection
Feedback Loops and Self-Fulfilling Bias in Recruitment
Demographic Parity Versus Individual Fairness Trade-offs
Temporal Drift of Bias in Hiring Algorithms
Intersectionality and Compounded Algorithmic Disadvantage
Opacity and Accountability in Black-Box Hiring Systems
Data Poisoning and Historical Bias Perpetuation Mechanisms
Algorithmic Redlining: Structural Exclusion in Job Markets
Fairness Metrics and Their Unintended Consequences
Human-Algorithm Collaboration and Bias Amplification

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