Algorithmic Justice in Financial Markets: A Normative Framework for Fair Trading in the Age of Artificial Intelligence

This study explores the issue of fairness in AI-driven algorithmic trading through an ethical lens that synthesizes Rawlsian justice theory, utilitarianism, and virtue ethics. By analyzing key events such as the 2010 Flash Crash and the GameStop incident, this study reveals how algorithmic trading helps professional institutions disadvantage retail investors. We will begin by reviewing the use of trading algorithms such as HFT in today's markets, as well as traditional theories and principles of market ethics. This paper will then articulate the theoretical framework, discuss the fairness challenges posed by algorithmic trading, and incorporate case studies. The article will conclude with recommendations, including mandatory transparency protocols for trading algorithms, latency standardization for achieving equal access, AI-driven real-time market monitoring, and independent certification systems that enforce the principle of "algorithmic justice." By redefining fairness as a necessary consideration during algorithm design rather than an afterthought to avoid market regulation, this study argues for integrating ethical responsibility into code architectures and regulatory infrastructures, balancing technological innovation with fair market participation.