Transparency issues in AI forex trading systems pose significant risks for traders, regulators, and financial institutions. One major concern is the black-box nature of AI models. Many AI-driven trading systems use complex algorithms that lack explainability, making it difficult for traders to understand how decisions are made. This lack of transparency increases the risk of blindly following AI-generated trade signals without fully grasping the potential risks. Another issue is hidden biases in AI models. If an AI system is trained on skewed historical data, it may favor specific trading strategies or currency pairs without clearly revealing these biases. This can lead to unfair market advantages and unpredictable trading outcomes. Regulatory challenges also arise due to transparency issues. Financial authorities struggle to monitor AI-driven forex trading activities because algorithms evolve dynamically. Without clear oversight, AI systems could engage in unethical or manipulative trading behaviors without immediate detection. To improve transparency, traders and regulators must push for explainable AI (XAI), requiring AI models to provide interpretable decision-making processes. Regular audits, bias detection mechanisms, and clear risk disclosures are essential to ensuring responsible AI-driven forex trading.