Oyeniran, Adewusi, Adeleke, Akwawa, & Azubuko, P. 115-126 Page 116 implementing robust auditing processes. We also review existing ethical guidelines and frameworks, such as those proposed by IEEE and the European Union, which provide a foundation for ethical AI development. Challenges in identifying and addressing bias are examined, such as the trade-offs between fairness and model accuracy, and the complexities of legal and regulatory requirements. Future directions are considered, including emerging trends in ethical AI, the importance of interdisciplinary collaboration, and innovations in bias detection and mitigation. In conclusion, ongoing vigilance and commitment to ethical practices are essential for developing AI systems that are equitable and just. This paper calls for continuous improvement and proactive measures from developers, researchers, and policymakers to create AI technologies that serve all individuals fairly and without bias.