Algorithmic trading has revolutionized financial markets by leveraging Artificial Intelligence (AI) and machine learning (ML) to execute high-speed, data-driven trading strategies. However, the complexity and opacity of AI-driven trading models pose significant challenges related to interpretability, regulatory compliance, and bias mitigation. Explainable AI (XAI) offers a solution by enhancing the transparency of algorithmic decision-making, allowing market participants, regulators, and investors to understand the rationale behind AI-generated trading decisions. The increasing reliance on black-box AI models raises concerns over unintended biases, which can result in market distortions, unfair trading advantages, and regulatory violations. Bias in algorithmic trading arises from skewed training data, model overfitting, and systemic reinforcement of historical inefficiencies. Without proper oversight, AI models may perpetuate discriminatory practices, leading to unintended financial disparities and potential legal repercussions. Implementing XAI techniques, such as feature importance analysis, counterfactual explanations, and model auditing, can help mitigate these risks. By providing greater interpretability, XAI facilitates compliance with evolving financial regulations, such as the European Union’s AI Act and the U.S. Securities and Exchange Commission’s (SEC) fairness and accountability guidelines. Financial institutions can leverage XAI to build more ethical, robust, and compliant trading models, ensuring market integrity and investor confidence. Future research should focus on developing standardized XAI frameworks tailored to financial markets, integrating fairness-aware ML techniques, and fostering collaboration between regulators and AI developers. The adoption of explainable AI in algorithmic trading is crucial for promoting ethical AI deployment, minimizing systemic risks, and ensuring fair market participation.