Algorithmic Bias and Social Inequality in AI Decision-Making Systems From a Sociological Perspective

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This research demonstrates how AI decision-making systems perpetuate social inequality, highlighting critical biases in various domains.

Algorithmic bias underscores existing social inequalities, influencing fairness and equity in AI systems.
The study illustrates disparities in AI applications across domains such as criminal justice and health care.
Observational analysis reviews sociological theories like intersectionality and critical race theory to understand bias mechanisms.
Findings suggest an interdisciplinary approach is essential to address and mitigate algorithmic bias effectively.

Raghbendra Jha (2024) studied this question.

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View Full Paper

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Authors

This research demonstrates how AI decision-making systems perpetuate social inequality, highlighting critical biases in various domains.

Algorithmic bias underscores existing social inequalities, influencing fairness and equity in AI systems.
The study illustrates disparities in AI applications across domains such as criminal justice and health care.
Observational analysis reviews sociological theories like intersectionality and critical race theory to understand bias mechanisms.
Findings suggest an interdisciplinary approach is essential to address and mitigate algorithmic bias effectively.

Raghbendra Jha (2024) studied this question.

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Bookmark