How Explainable AI Reduces Bias
Keywords:
interpretability, trustworthiness, AIAbstract
This research paper investigates the role of Explainable Artificial Intelligence (XAI) in reducing bias in AI systems. As AI becomes increasingly prevalent in decision-making processes across various domains, concerns about algorithmic bias have grown. This study explores how XAI techniques can be leveraged to identify, mitigate, and prevent bias in AI models. Through a comprehensive analysis of existing literature, implementation of XAI models, and evaluation of their effectiveness in bias reduction, this research contributes to the ongoing efforts to develop more fair and transparent AI systems. The findings demonstrate that XAI techniques, when properly applied, can significantly reduce bias in AI models while improving their interpretability and trustworthiness.
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