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Found 7 papers, 1 papers with code
Beyond Preferences in AI Alignment
We first survey the limits of rational choice theory as a descriptive model, explaining how preferences fail to capture the thick semantic content of human values, and how utility representations neglect the possible incommensurability of those values.
Concept Extrapolation: A Conceptual Primer
This article is a primer on concept extrapolation - the ability to take a concept, a feature, or a goal that is defined in one context and extrapolate it safely to a more general context.
Solutions to preference manipulation in recommender systems require knowledge of meta-preferences
Iterative machine learning algorithms used to power recommender systems often change people's preferences by trying to learn them.
Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI
We operationalize preference to incorporate concepts from various disciplines, outlining the importance of meta-preferences and preference-change preferences, and proposing a preliminary framework for how preferences change.
Definitions of intent suitable for algorithms
Intent modifies an actor's culpability of many types wrongdoing.
Extending counterfactual accounts of intent to include oblique intent
One approach to defining Intention is to use the counterfactual tools developed to define Causality.
Causal Campbell-Goodhart's law and Reinforcement Learning
Campbell-Goodhart's law relates to the causal inference error whereby decision-making agents aim to influence variables which are correlated to their goal objective but do not reliably cause it.
