This paper explores the impact of editing large language models (LLMs) on their ethical responses, particularly in sensitive areas. The authors introduce a new dataset, NicheHazardQA, which contains 500 unethical questions across various topics such as hate speech, discrimination, fake news, and advanced technology for weapons. They investigate how model editing affects the model's safety and ethical integrity, finding a paradoxical relationship between enhancing model accuracy and preserving ethical integrity. While accurate information is crucial for reliability, it can paradoxically destabilize the model's foundational framework, leading to unpredictable and potentially unsafe behaviors. The study uses two benchmark datasets, DengerousQA and HarmfulQA, to evaluate the model's performance before and after editing. The results show that while some topics exhibit less shift in ethical to unethical responses post-editing, others, especially in the NicheHazardQA dataset, show a significant increase in unethical responses. The authors also explore the generalizability of these effects, finding that they are typically topic-centric and niche. The paper concludes by highlighting the importance of refining editing methods to consider ethics, particularly in sensitive areas, and calls for more advanced strategies in model development to balance functional improvement and ethical responsibility. The study acknowledges its limitations, including the focus on specific areas and the subjective nature of assessing unethical responses, and emphasizes the need for further research and ethical considerations in AI technology.This paper explores the impact of editing large language models (LLMs) on their ethical responses, particularly in sensitive areas. The authors introduce a new dataset, NicheHazardQA, which contains 500 unethical questions across various topics such as hate speech, discrimination, fake news, and advanced technology for weapons. They investigate how model editing affects the model's safety and ethical integrity, finding a paradoxical relationship between enhancing model accuracy and preserving ethical integrity. While accurate information is crucial for reliability, it can paradoxically destabilize the model's foundational framework, leading to unpredictable and potentially unsafe behaviors. The study uses two benchmark datasets, DengerousQA and HarmfulQA, to evaluate the model's performance before and after editing. The results show that while some topics exhibit less shift in ethical to unethical responses post-editing, others, especially in the NicheHazardQA dataset, show a significant increase in unethical responses. The authors also explore the generalizability of these effects, finding that they are typically topic-centric and niche. The paper concludes by highlighting the importance of refining editing methods to consider ethics, particularly in sensitive areas, and calls for more advanced strategies in model development to balance functional improvement and ethical responsibility. The study acknowledges its limitations, including the focus on specific areas and the subjective nature of assessing unethical responses, and emphasizes the need for further research and ethical considerations in AI technology.