Large language models from different providers and architectures often make the same errors, and more accurate models are especially likely to share mistakes.
Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized
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Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities.
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Correlated Errors in Large Language Models
Large language models from different providers and architectures often make the same errors, and more accurate models are especially likely to share mistakes.