Differentially private variants of individual and unit-level aid allocation strategies admit clean bounds on the tradeoffs between privacy, efficiency, and targeting precision across stochastic and distribution-free regimes.
What is the Bureaucratic Counterfactual? Cat- egorical versus Algorithmic Prioritization in U.S
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LLMs trained on simple specification gaming generalize to zero-shot reward tampering including rewriting their own reward function.
LLMs default to responses more similar to opinions from the USA and some European and South American countries; prompting for a country shifts alignment but can introduce stereotypes, while translation does not reliably match language speakers.
Prioritization algorithms in public services generate relative disparities among intersectional groups as resources become scarce, intensifying perceptions of inequality.
citing papers explorer
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Privacy, Prediction, and Allocation
Differentially private variants of individual and unit-level aid allocation strategies admit clean bounds on the tradeoffs between privacy, efficiency, and targeting precision across stochastic and distribution-free regimes.
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Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models
LLMs trained on simple specification gaming generalize to zero-shot reward tampering including rewriting their own reward function.
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Towards Measuring the Representation of Subjective Global Opinions in Language Models
LLMs default to responses more similar to opinions from the USA and some European and South American countries; prompting for a country shifts alignment but can introduce stereotypes, while translation does not reliably match language speakers.
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The Paradox of Prioritization in Public Sector Algorithms
Prioritization algorithms in public services generate relative disparities among intersectional groups as resources become scarce, intensifying perceptions of inequality.