Equalizing recourse across groups
WebWe measure recourse as the distance of an individual from the decision boundary of a classifier. We then introduce a regularized objective to minimize the difference in … WebSep 6, 2024 · We measure recourse as the distance of an individual from the decision boundary of a classifier. We then introduce a regularized objective to minimize the …
Equalizing recourse across groups
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WebOn February 21, 2024, Boies Schiller Flexner LLP and a distinguished legal team including attorneys from law firms across the country filed four lawsuits in four states—California, … WebSep 7, 2024 · In this paper, we build classifiers that maintain prediction accuracy while also ensuring opportunities for feasible recourse across groups. To do this, we define a …
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WebOct 13, 2024 · Equalizing Recourse across Groups Vivek Gupta, Pegah Nokhiz, Chitradeep Dutta Roy, S. Venkatasubramanian Computer Science ArXiv 2024 TLDR This paper describes how to build models that make accurate predictions while still ensuring that the penalties for a negative outcome do not disadvantage different groups … WebArxiv 19: Equalizing recourse across groups 基础的recourse测量的是单个样本,本文给出了一个group级别的recourse度量。 方法思路很简单,对每一个负样本即h(x^F)=-1,我们定义一个关于他的recourse cost为 r^{IMF}=\min_{x\in\mathcal{X}}d(x^F,x) s.t. h(x)=1 \\ 即与x^F距离最近的的正样本的距离。 那么对一个group,我们就能计算所有负样本的IMF距 …
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Web- "Equalizing Recourse across Groups" Figure 3: Linear (upper) and polynomial (lower) kernels’ results on recourse and accuracy. Yellow circle is the mean, and the orange line is the median. autohotkey fn 键WebWe measure recourse as the distance of an individual from the decision boundary of a classifier. We then introduce a regularized objective to minimize the difference in … gb 17675 2021WebSep 7, 2024 · This paper describes how to build models that make accurate predictions while still ensuring that the penalties for a negative outcome do not disadvantage … autohotkey gui menuWebOct 17, 2024 · Search ACM Digital Library. Search Search. Advanced Search autohotkey gui buttonsWebOct 17, 2024 · 'Algorithmic recourse' offers feasible recovery actions to change unwanted outcomes through the modification of attributes. We introduce the notion of ranked group-level recourse fairness, and develop a 'recourse-aware ranking' solution that satisfies ranked recourse fairness constraints while minimizing the cost of suggested modifications. gb 17733WebEqualizing Recourse across Groups Preprint Full-text available Sep 2024 Vivek Gupta Pegah Nokhiz Chitradeep Dutta Roy Suresh Venkatasubramanian The rise in machine learning-assisted... gb 17713WebMar 1, 2024 · ABSTRACT. As machine learning is increasingly used to inform consequential decision-making (e.g., pre-trial bail and loan approval), it becomes important to explain … autohotkey home assistant