Machine Learning in Automated Decision Systems with Fairness Awareness
Steven Henry
Ladoke Akintola University of Technology
Abstract
Machine learning is increasingly used in important areas such as employment, healthcare, criminal justice, and banking. However, concerns have been raised that these systems may reinforce or amplify existing social biases. This paper discusses Fairness-Aware Machine Learning (FAML), an emerging field aimed at improving the fairness of machine learning models by incorporating constraints and fairness-aware design principles. We examine key fairness concepts including equal opportunity and equalized odds. In addition, we review algorithmic strategies for reducing bias through pre-processing, in-processing, and post-processing techniques. The study also explores the relationship between fairness, transparency, and accountability in automated decision-making systems. Through case studies and theoretical analysis, we demonstrate that integrating fairness considerations into machine learning systems can significantly reduce bias, improve public trust, and enhance system accountability. Finally, we outline key challenges and propose future research directions to support the development of ethical and socially responsible automated decision systems.
Keywords
Fairness-aware machine learning, Algorithmic bias, Automated decision systems, Discrimination mitigation, Ethical AI, Fairness metrics, Socio-technical systems, Responsible AI.
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