Abstract

The growing use of artificial intelligence (AI) systems in decision-making across various domains has raised critical concerns about bias, fairness, and transparency. AI algorithms can inadvertently perpetuate biases based on the data they are trained on, resulting in outcomes that disproportionately affect certain groups. This paper proposes new methods for detecting and mitigating bias in AI systems while ensuring greater algorithmic transparency. The focus is on developing innovative approaches to identify bias at multiple stages of AI development, from data collection to model deployment. Additionally, the paper emphasizes the need for transparent AI models that allow for explainability and accountability in decision-making. The proposed methods include novel fairness metrics, new tools for detecting biases in datasets, and frameworks for ensuring transparency through explainable AI (XAI) techniques.