The value-loading problem is a tough challenge in AI development: how do we ensure that superintelligent AI agents are aligned with human values and preferences? Solving this problem will require a cross-disciplinary approach, covering not only machine learning and philosophy, but also fields such as psychology, neuroscience, and economics.
This week, we’ve invited Nathan Henry to discuss his latest paper on the HALO algorithm (Hormetic ALignment via Opponent processes). HALO is designed to set healthy limits for repeatable AI behaviors and represents a cross-disciplinary approach to the value-loading problem. Nathan will demonstrate how HALO applies the psychological concept of behavioral hormesis: the idea that low frequencies of a behavior can be beneficial, while high frequencies can be harmful.
We’ll also discuss some potential current applications of the HALO algorithm. For example, HALO may be used to reduce positive feedback loops in recommendation systems. Such an approach may help to combat echo chambers (or filter bubbles) on social media and video-sharing platforms such as Facebook, YouTube, and Netflix. More broadly, we will discuss why knowledge from fields such as psychology and economics will be crucial to achieving true AI alignment.
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.