UTTAR PRADESH JOURNAL OF ZOOLOGY

Microbial resistance has long been linked to antibiotic resistance, a serious worldwide health issue. But as technology advances quickly in this day and age, a related phenomenon in the field of artificial intelligence [AI] is beginning to take shape. In order to better understand the idea of "antibiotic resistance" in the context of artificial intelligence [AI], this study will compare and contrast the evolution of bacterial resistance with potential obstacles in the design and implementation of intelligent systems. The increasing prevalence of AI systems across several industries highlights the striking similarities between their capacity to adapt and withstand hostile attacks and changing surroundings, and the biological resistance mechanisms seen in bacteria. This study explores the causes behind AI resistance, looking at how data drift, adversarial manipulations, and changing user behavior might cause machine learning systems to lose their effectiveness over time. The paper also examines the ethical ramifications of AI resistance, addressing issues with biases, unforeseen outcomes, and the influence of intelligent systems on society that are resistant to change or intervention. The area of antibiotic stewardship in medicine serves as an inspiration for the paper's discussion of potential mitigation techniques for AI resistance. Through the identification of parallels between AI resistance and antibiotic resistance in bacteria, this study adds to a better comprehension of the difficulties pertaining to the long-term viability and efficiency of intelligent systems. Since AI will continue to be a major influence on the future, it is critical to address the problem of "antibiotic resistance" in this context in order to ensure that AI is developed responsibly and ethically.