Large language models (LLMs) are increasingly powerful, but with great power comes great responsibility—especially when it comes to fairness and privacy. A new research paper reveals a surprising conflict: improving an LLM’s privacy protections can actually make it *less* fair. This counter-intuitive trade-off emerges when using supervised fine-tuning (SFT) with limited data, a common scenario in real-world applications. The paper proposes a clever solution called DEAN (Deactivating the Coupled Neurons) to address this conflict. Inspired by information theory, DEAN identifies and neutralizes the specific neurons in the LLM that are responsible for linking fairness and privacy. By decoupling these concepts at the neural level, DEAN allows LLMs to improve in both areas simultaneously. Extensive experiments demonstrate that DEAN boosts both fairness and privacy awareness without sacrificing overall performance. What's particularly exciting is DEAN’s robustness. It works effectively even with limited or even "malicious" training data (data that could actually worsen biases if used for traditional fine-tuning). This resilience makes DEAN a promising tool for building more ethical and responsible AI, especially in sensitive applications like healthcare and finance where both fairness and privacy are paramount. While DEAN isn't a silver bullet, it represents a significant step forward in navigating the complex ethical landscape of LLMs. Future research could explore even more fine-grained control over LLM behavior, leading to AI systems that are both powerful and principled.