Taking Principles Seriously: A Hybrid Approach to Value Alignment

TL;DR

This paper introduces a hybrid approach to AI value alignment that combines ethical reasoning with empirical observation, aiming to reflect valid ethical principles without falling into naturalistic fallacy.

Contribution

It proposes a formal framework using quantified model logic to integrate deontological ethics with empirical value alignment in AI systems.

Findings

Formulates ethical principles as test propositions in AI rule bases.

Demonstrates how empirical VA can validate ethical test propositions.

Provides a logical framework to avoid naturalistic fallacy in AI ethics.

Abstract

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.