Perceptions on AI Fairness in Financial Recommendation Engines - Pradipta, Naufal (Omnia Tempus, 2025)
This research aims to explore the implications of machine learning algorithms on fairness, particularly in sensitive applications such as criminal justice, healthcare, consumer finance, and hiring systems. The study examines how algorithmic biases can perpetuate social inequities, focusing on racial and gender disparities in automated decision-making processes. The research employs a qualitative approach through a comprehensive literature review, synthesizing findings from various case studies and articles that highlight algorithmic bias in real-world scenarios. The analysis discusses the impact of these biases, outlining the risks they present in shaping public perception and trust in AI technologies. Findings from the review emphasize the need for greater transparency in algorithmic models and the implementation of bias-correction strategies. The study also highlights the importance of ensuring fairness in AI-driven processes, particularly in contexts where life-altering decisions, such as hiring and healthcare, are made. Ultimately, the research calls for the development of ethical frameworks and regulatory measures that promote algorithmic fairness while safeguarding individuals' rights. This work contributes to the ongoing discourse on AI ethics and offers recommendations for policymakers, technologists, and organizations to address the challenges of algorithmic fairness.