This review article presents a conceptual framework for ethical AI development in IT systems, emphasizing the integration of ethical considerations throughout the AI development lifecycle. The framework incorporates multidisciplinary perspectives from ethics, computer science, law, and philosophy, focusing on transparency, accountability, fairness, privacy, and security. It aims to guide AI development and integration into IT systems by ensuring that AI technologies are designed, deployed, and governed in a manner that upholds ethical standards and aligns with societal values. The framework addresses the societal, ethical, and legal concerns arising from the rapid advancement of AI technologies. It highlights the need for ethical guidelines and standards to govern AI development and usage, particularly in light of algorithmic bias, data privacy violations, and AI-driven discrimination. The framework also emphasizes the importance of legal compliance, ensuring that AI systems adhere to relevant laws and regulations. Key components of the framework include transparency, which involves making AI algorithms and decision-making processes comprehensible and explainable to stakeholders. Accountability mechanisms are essential for attributing responsibility for AI-driven outcomes and facilitating recourse in cases of harm or injustice. Fairness is another core principle, advocating for the mitigation of biases and discrimination across diverse demographic groups. Privacy protection measures are crucial to safeguarding individuals' personal data, while robust security protocols are essential for defending against malicious exploitation and adversarial attacks. The framework is applied throughout the AI development lifecycle, from design and implementation to deployment and maintenance. During the design phase, ethical considerations are integrated into the architecture and specifications of AI systems. During the implementation phase, developers adhere to ethical coding practices and standards. During the deployment phase, AI systems are monitored for ethical compliance and performance. During the maintenance phase, AI systems are updated to reflect evolving ethical norms and standards. The future of ethical AI development in IT systems requires ongoing collaboration, innovation, and commitment from all stakeholders. Key challenges include addressing emerging ethical challenges, strengthening governance mechanisms, promoting diversity and inclusion, enhancing accountability and transparency, and fostering global collaboration. The adoption of a conceptual framework for ethical AI development is essential for guiding the responsible and ethical deployment of AI technologies in IT systems.This review article presents a conceptual framework for ethical AI development in IT systems, emphasizing the integration of ethical considerations throughout the AI development lifecycle. The framework incorporates multidisciplinary perspectives from ethics, computer science, law, and philosophy, focusing on transparency, accountability, fairness, privacy, and security. It aims to guide AI development and integration into IT systems by ensuring that AI technologies are designed, deployed, and governed in a manner that upholds ethical standards and aligns with societal values. The framework addresses the societal, ethical, and legal concerns arising from the rapid advancement of AI technologies. It highlights the need for ethical guidelines and standards to govern AI development and usage, particularly in light of algorithmic bias, data privacy violations, and AI-driven discrimination. The framework also emphasizes the importance of legal compliance, ensuring that AI systems adhere to relevant laws and regulations. Key components of the framework include transparency, which involves making AI algorithms and decision-making processes comprehensible and explainable to stakeholders. Accountability mechanisms are essential for attributing responsibility for AI-driven outcomes and facilitating recourse in cases of harm or injustice. Fairness is another core principle, advocating for the mitigation of biases and discrimination across diverse demographic groups. Privacy protection measures are crucial to safeguarding individuals' personal data, while robust security protocols are essential for defending against malicious exploitation and adversarial attacks. The framework is applied throughout the AI development lifecycle, from design and implementation to deployment and maintenance. During the design phase, ethical considerations are integrated into the architecture and specifications of AI systems. During the implementation phase, developers adhere to ethical coding practices and standards. During the deployment phase, AI systems are monitored for ethical compliance and performance. During the maintenance phase, AI systems are updated to reflect evolving ethical norms and standards. The future of ethical AI development in IT systems requires ongoing collaboration, innovation, and commitment from all stakeholders. Key challenges include addressing emerging ethical challenges, strengthening governance mechanisms, promoting diversity and inclusion, enhancing accountability and transparency, and fostering global collaboration. The adoption of a conceptual framework for ethical AI development is essential for guiding the responsible and ethical deployment of AI technologies in IT systems.