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Understanding Algorithmic Bias in Artificial Intelligence
0
Zitationen
1
Autoren
2026
Jahr
Abstract
This research paper looks into the causes, types, and social effects of AI bias. It examines how algorithms can unintentionally discriminate against certain groups, even when the data and developers are neutral. The paper discusses real-life examples, such as biased hiring tools and AI interview systems. It highlights strategies for reducing AI bias, including having diverse datasets, auditing algorithms, ensuring human oversight, and designing for fairness. Understanding and tackling AI bias is crucial for building ethical and fair AI systems.
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