Bias in Artificial Intelligence and Mitigation Strategies

This research article explores the various types of biases in artificial intelligence systems, including data, algorithmic, and selection biases. It discusses the implications of these biases on decision-making processes and offers comprehensive strategies for mitigation. Key strategies include ensuring diverse training data, implementing explainable AI, and conducting regular audits. The article aims to promote fairness, transparency, and accountability in AI applications across different secto