Value learning

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Value learning is a research area within artificial intelligence (AI) and AI alignment that focuses on building systems capable of inferring, acquiring, or learning human values, goals, and preferences from data, behavior, and feedback. The aim is to ensure that advanced AI systems act in ways that are beneficial and aligned with human well-being, even in the absence of explicitly programmed instructions.^[1][2]

Unlike traditional AI that focuses purely on task performance, value learning aims to ensure that AI decisions are ethically and socially acceptable. It is analogous to teaching a child right from wrong—guiding an AI to recognize which actions align with human moral standards and which do not. The process typically involves identifying relevant values (such as safety or fairness), collecting data that reflects those values, training models to learn appropriate responses, and iteratively refining their behavior through feedback and evaluation. Applications include minimizing harm in autonomous vehicles, promoting fairness in financial systems, prioritizing patient well-being in healthcare, and respecting user preferences in digital assistants. Compared to earlier techniques, value learning shifts the focus from mere functionality to understanding the underlying reasons behind choices, aligning machine behavior with human ethical expectations.^[3]