This post is written in my personal capacity, and does not necessarily represent the views of OpenAI or any other organization. Cross-posted to the Effective Altruism Forum.

This sequence of posts will argue that working to ensure that AI systems follow laws is a worthwhile way to improve the long-term future of AI.^[1]

The structure of this sequence will be as follows:

First, in this post, I will define some key terms and sketch what an ideal law-following AI ("LFAI") system might look like.
In the next few posts, I will explain why law-following might not emerge by default given the existing constellation of alignment approaches, financial objectives, and legal constraints, and explain why this is troubling.
Finally, I will propose some policy and technical routes to ameliorating these problems.

If the vision here excites you, and you would like to get funding to work on it, get in touch. I may be excited to recommend grants for people working on this, as long as it does not distract them from working on more important alignment issues.

Dall•E

Image by OpenAI's DALL·E.

Key Definitions

A law-following AI , or LFAI , is an AI system that is designed to rigorously comply with some defined set of human-originating rules ("laws"),^[2] using legal interpretative techniques,^[3] under the assumption that those laws apply to the AI in the same way that they would to a human. By "intrinsically motivated," I mean that the AI is motivated to obey those rules regardless of whether (a) its human principal wants it to obey the law,^[4] or (b) disobeying the law would be instrumentally valuable.^[5] (The Appendix to this post explores some possible conceptual issues with this definition of LFAI.)

I will compare LFAI with intent-aligned AI. The standard definition of "intent alignment" generally concerns only the relationship between some property of a human principal H and the actions of the human's AI agent A:

Jan Leike et al. define the "agent alignment problem" as "How can we create agents that behave in accordance with the user's intentions?"
Amanda Askell et al. define "alignment" as "the degree of overlap between the way two agents rank different outcomes."
Paul Christiano defines "AI alignment" as "A is trying to do what H wants it to do."
Richard Ngo endorses Christiano's definition.

Iason Gabriel does not directly define "intent alignment," but provides a taxonomy wherein a...

Law-Following AI

Law-Following AI