3. Emerging Risks and Ethics As AI trading becomes more autonomous, new challenges have emerged regarding human agency and market stability. Cognitive Alienation: There is a growing concern that as decision-making is increasingly outsourced to AI, human traders may lose their "ethical awareness" and grip on market reality (Suranaree Journal of Social Science, 2026). Adaptive Fragility: While AI increases efficiency, it can also create "herding behavior." When multiple autonomous systems converge on the same strategy, it can intensify systemic risks and market flash crashes (SEACEN Centre, 2026). Accountability Gap: Determining responsibility for an out-of-control agent remains a complex issue. Even if an agent behaves in an unanticipated way, programmers and organizations remain legally responsible for their actions (Wellman & Rajan, 2017). References MDPI. (2026). Stock Market Analysis, Forecasting, and Automated Trading Using Deep Learning. Cited by: 1 MDPI. (2026). AI Agents in Financial Markets: Architecture, Applications, and Systemic Implications. PMC. (2026). Artificial intelligence in financial market prediction: advancements in machine learning for stock price forecasting. SEACEN Centre. (2026). Role of Artificial Intelligence in Finance: Selective Literature Review and Implications for Asia's Financial Stability. Șerban, F., & Vrinceanu, B. P. (2026). Entropy-Filtered Machine Learning for Risk-Aware Algorithmic Trading and Portfolio Decision Making. Journal of Risk and Financial Management, 19(4), 283. Suranaree Journal of Social Science. (2026). AI in Algorithmic Trading: A Cybernetic and Ethical Perspective on Equality and Market Sustainability.