算法单一文化与社会福利

The Societal Impacts of Algorithmic Decision-Making Pub Date : 1900-01-01 DOI:10.1145/3603195.3603211

𝜃 𝜀

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摘要

证明。我们需要证明Fθ满足定义6.1中的可微性、渐近最优性和单调性条件。可微性:n个噪声样本εi/θ的任意实现的概率密度为∏n i=1 f (εi/θ)。令ε = [ε1/θ，…]， εn/θ]为噪声值向量，设M(π)任一个ε∈M(π)都能产生排序π的域。任意排列的概率π是

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Algorithmic Monoculture and Social Welfare

Proof. We need to show that Fθ satisfies the differentiability, asymptotic optimality, and monotonicity conditions in Definition 6.1. Differentiability: The probability density of any realization of the n noise samples εi/θ is ∏ n i=1 f (εi/θ). Let ε = [ε1/θ, ... , εn/θ] be the vector of noise values and let M(π) ⊆ Rn be the region such that any ε ∈ M(π) will produce the ranking π. The probability of any permutation π is

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The Societal Impacts of Algorithmic Decision-Making

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