#CommunityAMA Ensuring transparency in AI decision-making is essential for building trust and accountability in Forex trading. Many AI systems operate as black boxes, offering little to no explanation of how trade decisions are made. This lack of clarity poses a significant risk, especially for retail traders who may rely on these tools without understanding their underlying logic. Transparent AI models should provide interpretable outputs that explain why a trade was initiated, including which indicators or patterns were prioritized. Incorporating explainable AI (XAI) frameworks allows users to trace decisions back to their inputs, making it easier to evaluate model behavior under different conditions. Developers also bear responsibility for documenting model assumptions, training data characteristics, and the scope of a model’s capabilities and limitations. Regular audits and independent model reviews can further reinforce trust. Without transparency, AI can perpetuate errors, biases, or even unethical behavior without detection. In a market as complex and impactful as Forex, clarity in decision-making is not just a technical benefit—it’s a safeguard against manipulation, overconfidence, and systemic risk.