Mitigating Bias in AI: A Framework for Ethical and Fair Machine Learning Models

Abstract

The rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) in decision-making processes has highlighted significant concerns regarding bias and fairness. Bias in AI systems can lead to discriminatory outcomes, reinforcing societaBias Mitigation, Fairness in AI, Ethical AI, Algorithmic Bias, Explainable AI (XAI), Regulatory Compliance in AI, AI Governance, Social Impact of AI, AI Model Interpretability, Diversity in Training Datal inequalities. This paper presents a comprehensive framework for mitigating bias in AI, encompassing data preprocessing, model training, evaluation, and deployment strategies. We discuss techniques such as adversarial debiasing and fairness constraints to achieve this. We also delve into ethical considerations, regulatory implications, and best practices to ensure fairness in AI-driven decision-making. The framework aims to assist practitioners, researchers, and policymakers in developing more equitable and transparent AI systems, ultimately leading to fairer and more inclusive AI-driven decision-making.

Keywords: Bias Mitigation, Fairness in AI, Ethical AI, Algorithmic Bias, Explainable AI (XAI), Regulatory Compliance in AI, AI Governance, Social Impact of AI, AI Model Interpretability, Diversity in Training Data