AI in Algorithmic Trading: A Cybernetic and Ethical Perspective on Equality and Market Sustainability in the Thai Stock Market

Abstract

Background and Objectives: AI-driven algorithmic trading (AI-trading) is reshaping financial markets by improving efficiency and decision-making. Yet, its growing influence raises ethical concerns about human agency, inequality, and market sustainability. This paper aims to (1) examine how AI-trading reshapes the relationship between human traders and intelligent systems, particularly in the evolving context of cognitive alienation; and (2) assess how AI-trading contributes to inequality by reinforcing unequal access to cognitive and technological infrastructure. In addressing the second aim, the paper also considers whether AI-trading, through its reflexive and adaptive functions, should be viewed as a moral agent, providing an ethical basis for evaluating responsibility with respect to sustainability and governance. Methodology: This study adopts a qualitative and conceptual research design with a normative ethical orientation to examine the implications of AI-trading in financial markets. It is grounded in interdisciplinary perspectives, particularly cybernetics, philosophy of technology, and financial theory. The study develops a theoretical framework for understanding AI-trading as an embedded component of a socio-technical system, rather than as a purely technical tool. The research combines conceptual analysis with a case-based examination of the Thai capital market. The Stock Exchange of Thailand (SET) serves as an empirical context to illustrate how AI-trading systems interact with market structures and participant behavior. This design connects abstract ethical concerns, such as cognitive alienation and inequality, with observable patterns in financial systems. Results: The findings suggest that AI-trading is contributing to a developing state of human cognitive alienation, as decision-making processes are increasingly outsourced to autonomous systems. This shifts responsibility and weakens ethical awareness in financial practices. While AI can democratize expertise, it may also reinforce inequality by granting significant advantages to those with superior access to data, speed, and infrastructure. In Thailand, this trend is reflected in the declining participation of retail investors and the growing concentration of market share among brokerage firms, suggesting increasing systemic imbalances. Discussion: Evidence from the Thai stock market indicates rising market concentration alongside the growth of AI-trading, particularly as reflected in the increasing dominance of a single brokerage firm. While causality cannot be definitively established, parallel trends suggest a potential association between AI-trading and structural concentration. Scenario analysis highlights both potential benefits, such as improved efficiency and liquidity, and risks, including reduced participation, unintended collusion, and systemic instability. These developments challenge assumptions of market efficiency and raise ethical concerns regarding cognitive alienation and the evolving role of human agency in financial decision-making. Conclusions: Ethical oversight of AI-trading should extend beyond technical regulation to address its impact on human agency and market fairness. This study highlights the need to improve transparency, expand access to AI tools, and strengthen investor education to reduce inequality and mitigate cognitive alienation. In the Thai context, measures such as AI usage disclosure and real-time monitoring may help manage systemic risks. More broadly, ensuring that AI-trading supports market diversity and does not reinforce structural imbalances is essential for long-term sustainability. Future research should further explore cultural and behavioral factors shaping AI-trading adoption.