  We are currently unable to specify human goals and societal values in a way
that reliably directs AI behavior. Law-making and legal interpretation form a
computational engine that converts opaque human values into legible directives.
"Law Informs Code" is the research agenda embedding legal knowledge and
reasoning in AI. Similar to how parties to a legal contract cannot foresee
every potential contingency of their future relationship, and legislators
cannot predict all the circumstances under which their proposed bills will be
applied, we cannot ex ante specify rules that provably direct good AI behavior.
Legal theory and practice have developed arrays of tools to address these
specification problems. For instance, legal standards allow humans to develop
shared understandings and adapt them to novel situations. In contrast to more
prosaic uses of the law (e.g., as a deterrent of bad behavior through the
threat of sanction), leveraged as an expression of how humans communicate their
goals, and what society values, Law Informs Code.
  We describe how data generated by legal processes (methods of law-making,
statutory interpretation, contract drafting, applications of legal standards,
legal reasoning, etc.) can facilitate the robust specification of inherently
vague human goals. This increases human-AI alignment and the local usefulness
of AI. Toward society-AI alignment, we present a framework for understanding
law as the applied philosophy of multi-agent alignment. Although law is partly
a reflection of historically contingent political power - and thus not a
perfect aggregation of citizen preferences - if properly parsed, its
distillation offers the most legitimate computational comprehension of societal
values available. If law eventually informs powerful AI, engaging in the
deliberative political process to improve law takes on even more meaning.
