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Value alignment: a formal approach
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principles that should govern autonomous AI systems. It essentially states that a system's goals and behaviour should be aligned with human values. But how to ensure value alignment? In this paper we first provide a formal model to represent values through preferences and ways to compute value aggregations; i.e. preferences with respect to a group of agents and/or preferences with respect to sets of values. Value alignment is then defined, and computed, for a given norm with respect to a given value through the increase/decrease that it results in the preferences of future states of the world. We focus on norms as it is norms that govern behaviour, and as such, the alignment of a given system with a given value will be dictated by the norms the system follows.
Forward citations
Cited by 3 Pith papers
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Toward a Theory of Value in AI Alignment
A systematic annotation of 94 AI alignment papers shows the field largely equates human values with measurable preferences, rarely defines values, and is increasingly removing humans from alignment evaluation.
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Can an AI Agent Safely Run a Government? Existence of Probably Approximately Aligned Policies
Given a sufficiently accurate model of social dynamics, there provably exist policies that are near-optimal for a chosen social welfare function with high probability, plus a safety filter for arbitrary black-box policies.
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The Morality of Probability: How Implicit Moral Biases in LLMs May Shape the Future of Human-AI Symbiosis
Six large language models consistently rated care and virtue outcomes as most moral and libertarian outcomes as least moral across 54 AI-generated dilemma variants, with reasoning models more context-sensitive but les...
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