  An important step in the development of value alignment (VA) systems in AI is
understanding how VA can reflect valid ethical principles. We propose that
designers of VA systems incorporate ethics by utilizing a hybrid approach in
which both ethical reasoning and empirical observation play a role. This, we
argue, avoids committing the "naturalistic fallacy," which is an attempt to
derive "ought" from "is," and it provides a more adequate form of ethical
reasoning when the fallacy is not committed. Using quantified model logic, we
precisely formulate principles derived from deontological ethics and show how
they imply particular "test propositions" for any given action plan in an AI
rule base. The action plan is ethical only if the test proposition is
empirically true, a judgment that is made on the basis of empirical VA. This
permits empirical VA to integrate seamlessly with independently justified
ethical principles.
