  The value-loading problem is a significant challenge for researchers aiming
to create artificial intelligence (AI) systems that align with human values and
preferences. This problem requires a method to define and regulate safe and
optimal limits of AI behaviors. In this work, we propose HALO (Hormetic
ALignment via Opponent processes), a regulatory paradigm that uses hormetic
analysis to regulate the behavioral patterns of AI. Behavioral hormesis is a
phenomenon where low frequencies of a behavior have beneficial effects, while
high frequencies are harmful. By modeling behaviors as allostatic opponent
processes, we can use either Behavioral Frequency Response Analysis (BFRA) or
Behavioral Count Response Analysis (BCRA) to quantify the hormetic limits of
repeatable behaviors. We demonstrate how HALO can solve the 'paperclip
maximizer' scenario, a thought experiment where an unregulated AI tasked with
making paperclips could end up converting all matter in the universe into
paperclips. Our approach may be used to help create an evolving database of
'values' based on the hedonic calculus of repeatable behaviors with decreasing
marginal utility. This positions HALO as a promising solution for the
value-loading problem, which involves embedding human-aligned values into an AI
system, and the weak-to-strong generalization problem, which explores whether
weak models can supervise stronger models as they become more intelligent.
Hence, HALO opens several research avenues that may lead to the development of
a computational value system that allows an AI algorithm to learn whether the
decisions it makes are right or wrong.
