  Given that Artificial Intelligence (AI) increasingly permeates our lives, it
is critical that we systematically align AI objectives with the goals and
values of humans. The human-AI alignment problem stems from the impracticality
of explicitly specifying the rewards that AI models should receive for all the
actions they could take in all relevant states of the world. One possible
solution, then, is to leverage the capabilities of AI models to learn those
rewards implicitly from a rich source of data describing human values in a wide
range of contexts. The democratic policy-making process produces just such data
by developing specific rules, flexible standards, interpretable guidelines, and
generalizable precedents that synthesize citizens' preferences over potential
actions taken in many states of the world. Therefore, computationally encoding
public policies to make them legible to AI systems should be an important part
of a socio-technical approach to the broader human-AI alignment puzzle. This
Essay outlines research on AI that learn structures in policy data that can be
leveraged for downstream tasks. As a demonstration of the ability of AI to
comprehend policy, we provide a case study of an AI system that predicts the
relevance of proposed legislation to any given publicly traded company and its
likely effect on that company. We believe this represents the "comprehension"
phase of AI and policy, but leveraging policy as a key source of human values
to align AI requires "understanding" policy. Solving the alignment problem is
crucial to ensuring that AI is beneficial both individually (to the person or
group deploying the AI) and socially. As AI systems are given increasing
responsibility in high-stakes contexts, integrating democratically-determined
policy into those systems could align their behavior with human goals in a way
that is responsive to a constantly evolving society.
