A Study on the Ethical Implications of Algorithmic Bias in Automated Decision Support Systems
Keywords:
Algorithmic Bias, Ethics, Automated Decision Systems, Fairness, Transparency, AccountabilityAbstract
Automated Decision Support Systems (ADSS) are increasingly used across sectors such as healthcare, finance, criminal justice, and human resource management. While these systems promise efficiency and objectivity, they also introduce ethical concerns related to algorithmic bias, fairness, accountability, and transparency. Such biases often arise from flawed data, systemic inequalities, or biased model design, resulting in disproportionate harm to vulnerable populations. This paper examines the ethical implications of algorithmic bias, evaluates foundational literature, and discusses frameworks for ethical governance. A conceptual diagram and comparative tables illustrate key issues and responsible mitigation approaches
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