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Artificial intelligence (AI) is revolutionizing the world of research, posing unforeseen opportunities and imminent ethical challenges. This article redefines the ethic of research in AI by advocating for a shift toward a contextual, fair, and accountable approach. It examines critically how the existing standards of ethics are inadequate in addressing the complexity, scale, and lack of transparency of AI systems that are increasingly being used in research settings. By qualitative analysis of recent applications of AI in domains, the paper delineates core ethical issues, including undermining data privacy, algorithmic bias, incomprehensible decision-making, and diminishing accountability. These issues are enhanced in critical domains such as health care, social sciences, and public governance, where ethical errors have the potential to reinforce system injustice or victimize vulnerable populations. The paper argues that ethical responsibility in AI research has to be non-uniform. Rather, it calls for contextual ethics attuned to specific disciplines and cultural milieux in conjunction with justice-based methods that actively work against bias and inequality. Accountability must be designed into research design, not appended, through enforceable policy, continuous ethics education, and multidisciplinary governance. Suggestions include integrating AI-specific ethical norms into institutional policy, fostering open dialogue across disciplines, and developing an ethics culture of foresight. By encouraging a contextual, ethical approach, the research community can ensure that AI does not just accelerate discovery but does so in an equitable, transparent, and socially responsible fashion.

Artificial intelligence (AI) is revolutionizing the world of research, posing unforeseen opportunities and imminent ethical challenges. This article redefines the ethic of research in AI by advocating for a shift toward a contextual, fair, and accountable approach. It examines critically how the existing standards of ethics are inadequate in addressing the complexity, scale, and lack of transparency of AI systems that are increasingly being used in research settings. By qualitative analysis of recent applications of AI in domains, the paper delineates core ethical issues, including undermining data privacy, algorithmic bias, incomprehensible decision-making, and diminishing accountability. These issues are enhanced in critical domains such as health care, social sciences, and public governance, where ethical errors have the potential to reinforce system injustice or victimize vulnerable populations. The paper argues that ethical responsibility in AI research has to be non-uniform. Rather, it calls for contextual ethics attuned to specific disciplines and cultural milieux in conjunction with justice-based methods that actively work against bias and inequality. Accountability must be designed into research design, not appended, through enforceable policy, continuous ethics education, and multidisciplinary governance. Suggestions include integrating AI-specific ethical norms into institutional policy, fostering open dialogue across disciplines, and developing an ethics culture of foresight. By encouraging a contextual, ethical approach, the research community can ensure that AI does not just accelerate discovery but does so in an equitable, transparent, and socially responsible fashion.