Bias Checker AI Web Application: A Framework for Identifying Bias in AI Models

Abstract— Artificial Intelligence (AI) models are widely deployed in decision-making systems, but they often exhibit bias due to skewed training data or inherent algorithmic issues. This paper presents a Bias Checker AI Web Application designed to analyze and detect biases in AI-generated outputs. The system uses natural language processing (NLP) and statistical analysis techniques to assess potential biases in text-based predictions. The web-based interface enables [1] real-time bias evaluation, ensuring transparency and fairness in AI systems. The proposed system provides a user-friendly platform for developers and stakeholders to assess their models and mitigate discriminatory outcomes. Additionally, this paper explores the ethical implications of biased AI, potential mitigation techniques, and the importance of transparency in AI-driven decision-making processes. The issue of AI bias extends beyond technical flaws, influencing societal and economic structures by reinforcing stereotypes and discriminatory practices. Addressing bias in AI models is crucial for ensuring fairness in automated decision- making. As AI continues to permeate sectors like finance, healthcare, and law enforcement, biased models can perpetuate historical injustices, leading [14] to tangible negative consequences for marginalized groups. This paper emphasizes the role of bias detection tools in fostering trust and accountability in AI applications. Furthermore, we discuss the significance of incorporating explainability in AI-driven bias detection. The Bias Checker AI Web Application aims to bridge the gap between technical bias analysis and user interpretability, ensuring that results are accessible to both developers and non-technical stakeholders. By integrating intuitive visualization tools and user feedback mechanisms, our system enhances the accessibility of bias detection methodologies. Keywords: Bias detection, AI fairness, Natural Language Processing, Machine Learning, Web Application, Ethical AI, Algorithmic Transparency, AI Ethics.