Self-adaptive systems increasingly operate in close interaction with humans, often sharing the same physical or virtual environments and making decisions with ethical implications at runtime. Current approaches typically encode ethics as fixed, rule-based constraints or as a single chosen ethical theory embedded at design time. This overlooks a fundamental property of human-system interaction settings: ethical preferences vary across individuals and groups, evolve with context, and may conflict, while still needing to remain within a legally and regulatorily defined hard-ethics envelope (e.g., safety and compliance constraints). This paper advocates a shift from static ethical rules to runtime ethical reasoning for self-adaptive systems, where ethical preferences are treated as runtime requirements that must be elicited, represented, and continuously revised as stakeholders and situations change. We argue that satisfying such requirements demands explicit ethics-based negotiation to manage ethical trade-offs among multiple humans who interact with, are represented by, or are affected by a system. We identify key challenges, ethical uncertainty, conflicts among ethical values (including human, societal, and environmental drivers), and multi-dimensional/multi-party/multi-driver negotiation, and outline research directions and questions toward ethically self-adaptive systems.