Author: Vikram Reddy
Abstract: Autonomous Artificial Intelligence (AI) systems have rapidly advanced, transforming industries and human interactions. However, the increasing complexity of these systems has brought forth concerns regarding bias and fairness. This paper examines the various types of biases that can manifest in autonomous AI systems, how they arise, and their implications. Through a case study approach, we explore real-world examples where bias has influenced AI outcomes and the resulting societal, ethical, and legal consequences. The paper proposes strategies to address fairness in AI systems, aiming to establish methodologies that ensure transparency, accountability, and equitable outcomes. We conclude by emphasizing the need for continuous research and regulation to combat bias and promote fairness in autonomous AI systems.
Keywords: Bias, fairness, autonomous AI, machine learning, ethical AI, case studies, transparency, accountability.
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