This review explores the ethical implications of integrating Artificial Intelligence (AI) into decision-making processes across various industries. AI has become a transformative force, enhancing efficiency and outcomes through machine learning, natural language processing, and robotics. However, its integration raises significant ethical concerns, including transparency, fairness, accountability, and bias. The opacity of AI algorithms makes it difficult to understand how decisions are made, leading to challenges in accountability and trust. Bias in AI systems can perpetuate or exacerbate existing inequalities, necessitating careful examination of training data, algorithmic design, and ongoing monitoring to ensure fairness and mitigate discrimination. The increasing reliance on AI in decision-making also raises concerns about accountability and responsibility. Determining who is ultimately responsible for AI-driven decisions is a complex ethical issue. Establishing clear frameworks for accountability is crucial to ensure that individuals, organizations, and developers share responsibility for the outcomes of AI decisions. Ethical considerations extend to the broader societal impact of AI, including job displacement, economic inequality, and the concentration of power. Balancing technological advancement with social responsibility is essential to ensure that AI benefits society as a whole. Key ethical principles in AI include transparency, fairness, and accountability. Transparent AI systems allow users and stakeholders to understand the rationale behind decisions, fostering trust. Addressing bias in AI requires a combination of ethical considerations, technical solutions, and diverse representation in AI development. Mitigating bias involves ethical guidelines, technical solutions, and regular audits to ensure fairness and reduce discrimination. The role of data in AI decision-making is critical, with ethical concerns around data privacy, consent, quality, and biases. Privacy is a fundamental ethical concern, requiring measures to safeguard sensitive information and ensure compliance with data protection regulations. Informed consent is essential for ethical data usage, allowing individuals to make informed decisions about their data. Biases in training data can lead to unfair outcomes, necessitating continuous monitoring and evaluation of datasets to ensure fairness and inclusivity. The impact of AI on society and individuals is significant, with concerns about job displacement, socioeconomic implications, discrimination, and social justice. Proactive measures, such as upskilling and reskilling initiatives, are necessary to address job displacement. Ethical AI design should prioritize fairness and justice, ensuring that algorithms are not influenced by gender, race, or socioeconomic factors. Transparency in AI decision-making processes is essential for external scrutiny and accountability. Regulatory frameworks and ethical standards are crucial for governing AI in decision-making processes. Current regulations often focus on data protection rather than the ethical aspects of AI. There is a need for regulations that specifically target the ethical dimensions of AI, ensuring responsible deployment and safeguarding against potential risks. Ethical AI frameworks emphasize transparency, fairness, accountability, and the prevention of discriminatory outcomes. Case studies highlight ethical dilemmas in AI decision-making, such as the use of facial recognition technology and biased algorithms in hiring. These cases underscore the importance of transparency, inclusive design, and ethical frameworks in AI development. Public perception and trust inThis review explores the ethical implications of integrating Artificial Intelligence (AI) into decision-making processes across various industries. AI has become a transformative force, enhancing efficiency and outcomes through machine learning, natural language processing, and robotics. However, its integration raises significant ethical concerns, including transparency, fairness, accountability, and bias. The opacity of AI algorithms makes it difficult to understand how decisions are made, leading to challenges in accountability and trust. Bias in AI systems can perpetuate or exacerbate existing inequalities, necessitating careful examination of training data, algorithmic design, and ongoing monitoring to ensure fairness and mitigate discrimination. The increasing reliance on AI in decision-making also raises concerns about accountability and responsibility. Determining who is ultimately responsible for AI-driven decisions is a complex ethical issue. Establishing clear frameworks for accountability is crucial to ensure that individuals, organizations, and developers share responsibility for the outcomes of AI decisions. Ethical considerations extend to the broader societal impact of AI, including job displacement, economic inequality, and the concentration of power. Balancing technological advancement with social responsibility is essential to ensure that AI benefits society as a whole. Key ethical principles in AI include transparency, fairness, and accountability. Transparent AI systems allow users and stakeholders to understand the rationale behind decisions, fostering trust. Addressing bias in AI requires a combination of ethical considerations, technical solutions, and diverse representation in AI development. Mitigating bias involves ethical guidelines, technical solutions, and regular audits to ensure fairness and reduce discrimination. The role of data in AI decision-making is critical, with ethical concerns around data privacy, consent, quality, and biases. Privacy is a fundamental ethical concern, requiring measures to safeguard sensitive information and ensure compliance with data protection regulations. Informed consent is essential for ethical data usage, allowing individuals to make informed decisions about their data. Biases in training data can lead to unfair outcomes, necessitating continuous monitoring and evaluation of datasets to ensure fairness and inclusivity. The impact of AI on society and individuals is significant, with concerns about job displacement, socioeconomic implications, discrimination, and social justice. Proactive measures, such as upskilling and reskilling initiatives, are necessary to address job displacement. Ethical AI design should prioritize fairness and justice, ensuring that algorithms are not influenced by gender, race, or socioeconomic factors. Transparency in AI decision-making processes is essential for external scrutiny and accountability. Regulatory frameworks and ethical standards are crucial for governing AI in decision-making processes. Current regulations often focus on data protection rather than the ethical aspects of AI. There is a need for regulations that specifically target the ethical dimensions of AI, ensuring responsible deployment and safeguarding against potential risks. Ethical AI frameworks emphasize transparency, fairness, accountability, and the prevention of discriminatory outcomes. Case studies highlight ethical dilemmas in AI decision-making, such as the use of facial recognition technology and biased algorithms in hiring. These cases underscore the importance of transparency, inclusive design, and ethical frameworks in AI development. Public perception and trust in