Ethical principles for AI design must be translated into agreed-upon, machine-readable rules. These rules, often documented in DESIGN.md files, guide AI in making design decisions. While current files focus on aesthetics and functionality, there's a growing need to incorporate ethical considerations. Documenting accessibility standards, like WCAG compliance, is a step in this direction. However, extending this to broader ethical principles like inclusion, autonomy, transparency, privacy, and well-being presents a challenge. Human ethical interpretation relies on judgment, which AI lacks; it requires explicit, detailed instructions. Translating abstract ethical concepts into concrete, measurable rules that AI can follow is complex. Such rules need to be specific enough for AI to interpret consistently, similar to how accessibility standards provide measurable metrics. The process of defining and agreeing upon these ethical standards is crucial, mirroring the consensus-building behind accessibility guidelines. Conflicts between ethical principles, such as privacy versus transparency, add further complexity. Ultimately, the challenge lies not only in creating machine-readable ethical rules but also in establishing who has the authority to define them.