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Axioms for AI Alignment from Human Feedback
The problem of learning a reward function is one of preference aggregation that, we argue, largely falls within the scope of social choice theory.
Two-Sided Matching Meets Fair Division
We introduce a new model for two-sided matching which allows us to borrow popular fairness notions from the fair division literature such as envy-freeness up to one good and maximin share guarantee.
Fair Algorithms for Multi-Agent Multi-Armed Bandits
Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm"; indeed, each agent may perceive a different arm to be the best for her personally.
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